Use In Numerical Weather Prediction Research Articles

Aeolus Rayleigh‐channel winds in cloudy conditions

AbstractAeolus carried the first Doppler wind lidar to measure wind profiles from space. Aeolus was a European Space Agency explorer mission with the objective to retrieve winds data from the collected atmospheric return signal that is the result of Mie and Rayleigh scattering of laser emitted light by atmospheric molecules and particulates. During the course of the mission the quality of Aeolus winds measured in clear‐air conditions from Rayleigh‐channel‐collected data, so called Rayleigh‐clear winds, improved substantially. The same is true for winds measured in cloudy and aerosol‐rich atmospheric conditions from Mie‐channel‐collected data, the so‐called Mie‐cloudy winds. For the latter conditions, good quality winds can in principle also be obtained from Rayleigh‐channel‐collected data, the so‐called Rayleigh‐cloudy winds, if contamination of the purely molecular signal by Mie scattering is well addressed. We assess a linear and nonlinear correction for Mie contamination, the latter with the aid of numerical weather prediction model data for determing the correction parameters. We show that the nonlinear correction is able to provide unbiased Rayleigh‐cloudy winds. This makes Rayleigh‐cloudy winds suitable for use in numerical weather prediction, but also for direct comparison with other wind observations obtained in cloudy conditions, such as atmospheric motion wind vectors.

Fusing Numerical Weather Prediction Ensembles with Refractivity Inversions during Surface Ducting Conditions

Abstract This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island field experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared with this ground truth refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the ground truth parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.

D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts

AbstractCurrent practice in predicting future weather is the use of numerical weather prediction (NWP) models to produce ensemble forecasts. Despite of enormous improvements over the last few decades, they still tend to exhibit bias and dispersion errors and, consequently, lack calibration. Therefore, these forecasts may be improved by statistical postprocessing. In this work, we propose a D‐vine‐copula‐based postprocessing for 10 m surface wind speed ensemble forecasts. This approach makes use of quantile regression related to D‐vine copulas, which is highly data driven and allows one to adopt more general dependence structures as the state‐of‐the‐art zero‐truncated ensemble model output statistic (tEMOS) model. We compare local and global D‐vine copula quantile regression (DVQR) models to the corresponding tEMOS models and their gradient‐boosting extensions (tEMOS‐GB) for different sets of predictor variables using one lead time and 60 surface weather stations in Germany. Furthermore, we investigate which types of training periods can improve the performance of tEMOS and the D‐vine‐copula‐based method for wind speed postprocessing. We observe that the D‐vine‐based postprocessing yields a comparable performance with respect to tEMOS if only wind speed ensemble variables are included and to substantial refinements if additional meteorological and station‐specific weather variables are integrated. As our main result, we note that, in the global setting, DVQR is able to provide better scores than tEMOS‐GB in general, whereas the local DVQR is able to substantially outperform the local tEMOS‐GB at particular stations admitting nonlinear relationships among the variables. In addition, we remark that training periods capturing seasonal patterns perform the best. Last but not least, we adapt a criterion for calculating the variable importance in D‐vine copulas and we outline which predictor variables are due to this approach the most important for the correction of wind speed ensemble forecasts.

The Common Community Physics Package (CCPP) Framework v6

Abstract. The Common Community Physics Package (CCPP) is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model's dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and experimentation with physics–dynamics coupling methods while simultaneously offering capabilities for use in numerical weather prediction (NWP) operations. The CCPP Framework supports configurations ranging from process studies to operational NWP as it enables host models to assemble the parameterizations in flexible suites. Framework capabilities include variability in scheme call order; ability to group parameterizations for calls in different parts of the host model, allowing intervening computation or coupling to additional components; options to call some parameterizations more often than others; and automatic variable transformations. The CCPP Framework was developed by the Developmental Testbed Center and is distributed with a single-column model that can be used to test innovations and to conduct hierarchical studies in which physics and dynamics are decoupled. It is also an integral part of the Unified Forecast System, a community-based, coupled, comprehensive Earth modeling system designed to support research and be the source system for the NOAA's operational NWP applications. Finally, the CCPP Framework is under various stages of adoption by a number of other models in the wider community.

Forecasting the Ensemble Hydrograph of the Reservoir Inflow based on Post-Processed TIGGE Precipitation Forecasts in a Coupled Atmospheric-Hydrological System

The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood lead time. It is vital to post-process raw precipitation forecasts because of their significant bias when they feed hydrological models. In this research, ensemble precipitation forecasts (EPFs) of three NWP models (National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) (Exeter, UK), and Korea Meteorological Administration (KMA) (SEOUL, REPUBLIC OF KOREA)) were investigated for six historical storms leading to heavy floods in the Dez basin, Iran. To post-process EPFs, the raw output of every single NWP model was corrected using regression models. Then, two proposed models, the Group Method of Data Handling (GMDH) deep learning model and the Weighted Average–Weighted Least Square Regression (WA-WLSR) model, were employed to construct a multi-model ensemble (MME) system. The ensemble reservoir inflow was simulated using the HBV hydrological model under the two modeling approaches involving deterministic forecasts (simulation using observed precipitation data as input) and ensemble forecasts (simulation using post-processed EPFs as input). The results demonstrated that both GMDH and WA-WLSR models had a positive impact on improving the forecast skill of the NWP models, but more accurate results were obtained by the WA-WLSR model. Ensemble forecasts outperformed coupled atmospheric–hydrological modeling in comparison with deterministic forecasts to simulate inflow hydrographs. Our proposed approach lends itself to quantifying uncertainty of ensemble forecasts in hydrometeorological the models, making it possible to have more reliable strategies for extreme-weather event management.

Assessment of WRF microphysics and cumulus parameterizations in simulating heavy rainfall events over Badulu Oya catchment, Sri Lanka

Abstract Extreme rainfall events leading to severe hydrological impacts warrant an accurate prediction of such events not only on time but also in magnitude. Sri Lanka is a South Asian country that is frequently affected by severe tropical storms. The primary aim of this study was to improve heavy rainfall events forecast during the North-East monsoon over the Badulu Oya catchment, Sri Lanka. This aim was accomplished by simulating precipitation for two extreme North-East monsoon rainfall events using the Weather Research and Forecasting (WRF-ARW) model. A detailed comparison was made between the 24-h spatial distribution of model rainfall and observations obtained from rainfall gauges. Verification was evaluated based on three deterministic approaches. Each rainfall event was simulated multiple times using 15 different parameterization scheme combinations including six microphysics and four cumulus schemes at a 3 km grid resolution. The filtered best model combinations were validated using observations from another two heavy North-East monsoon rainfall events. The key finding from these evaluations was that model configurations with WSM5, WSM6, Kessler and WDM6 microphysics, and KF, BMJ and MKF cumulus schemes displayed the overall best performances. Therefore, these combinations have a good potential for operational use in numerical weather prediction over the said catchment.

Dependency of Mesoscale Organization on Grid Anisotropy in Large‐Eddy Simulations of Convective Boundary Layers at Gray Zone Resolutions

AbstractA new generation of operational atmospheric models operating at horizontal resolutions in the range 200 m ∼ 2 km is becoming increasingly popular for operational use in numerical weather prediction and climate applications. Such grid spacings are becoming sufficiently fine to resolve a fraction of the turbulent transports. Here we analyze Large‐eddy simulation results of a convective boundary layer obtained by coarsening horizontal grid spacings up to 800 m. The aim is to explore the dependency of the mean state and turbulent fluxes on the grid resolution. Both isotropic and anisotropic eddy diffusion approaches are evaluated, where in the latter case the horizontal and vertical eddy diffusivities differ in accord with their horizontal and vertical grid spacings. For coarsening horizontal grid sizes entrainment at the top of the boundary layer tends to get slightly enhanced for isotropic diffusion, whereas for the anisotropic diffusion approach the vertically well‐mixed boundary‐layer structure becomes severely degraded. An analysis of the energy spectrum shows that anisotropic diffusion causes relatively more dissipation of variance at smaller length scales. This leads, in turn, to a shift of spectral energy toward larger length scales that also becomes apparent from a rather different kind of spatial organization of convection. The present study therefore suggests that details with regards to the representation of processes at small scales might impact the organization at length scales much larger than the smallest scales that can be resolved by the model.

Using deep learning to emulate and accelerate a radiative-transfer model

AbstractThis paper describes the development of U-net++ models, a type of neural network that performs deep learning, to emulate the shortwave Rapid Radiative-transfer Model (RRTM). The goal is to emulate the RRTM accurately in a small fraction of the computing time, creating a U-net++ that could be used as a parameterization in numerical weather prediction (NWP). Target variables are surface downwelling flux, top-of-atmosphere upwelling flux (), net flux, and a profile of radiative-heating rates. We have devised several ways to make the U-net++ models knowledge-guided, recently identified as a key priority in machine learning (ML) applications to the geosciences. We conduct two experiments to find the best U-net++ configurations. In Experiment 1, we train on non-tropical sites and test on tropical sites, to assess extreme spatial generalization. In Experiment 2, we train on sites from all regions and test on different sites from all regions, with the goal of creating the best possible model for use in NWP. The selected model from Experiment 1 shows impressive skill on the tropical testing sites, except four notable deficiencies: large bias and error for heating rate in the upper stratosphere, unreliable for profiles with single-layer liquid cloud, large heating-rate bias in the mid-troposphere for profiles with multi-layer liquid cloud, and negative bias at lowzenith angles for all flux components and tropospheric heating rates. The selected model from Experiment 2 corrects all but the first deficiency, and both models run ~104 times faster than the RRTM. Our code is available publicly.

Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.

Impact of assimilating Mode‐S EHS winds in the Met Office's high‐resolution NWP model

AbstractAircraft play an important role in providing in‐situ upper air observations for use in numerical weather prediction (NWP) models. Aircraft observations of wind, temperature and humidity have been routinely assimilated into NWP models for decades. More recently, the Met Office has installed a network of six receivers to collect Mode‐Select Enhanced Surveillance (Mode‐S EHS) broadcasts and derive wind and temperature information. The paper focuses on the assimilation of high‐frequency (sub‐hourly) Mode‐S winds into the Met Office's hourly cycling high‐resolution NWP model, which employs four‐dimensional‐variational data assimilation (4D‐Var). An observing system experiment (OSE) experiment was carried out to assess the impact of additionally assimilating Mode‐S wind data on the model forecasts in both summer and winter. It is shown that assimilating Mode‐S winds has a positive benefit on the forecast skill in the first 6 hr wind profiles, with an improvement of the mean bias and root mean square (RMS) errors of up to 0.5 m·s−1 in the first hour and 0.3 m·s−1 in the sixth hour. The assimilation of Mode‐S winds is also seen to be beneficial to the near‐surface‐focused UK NWP composite index, and to the hourly precipitation accumulations in fractions skill score (FSS) for forecasts up to 9 hr ahead.

Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Wind power generation rapidly grows worldwide with declining costs and the pursuit of decarbonised energy systems. However, the utilization of wind energy remains challenging due to its strong stochastic nature. Accurate wind power forecasting is one of the effective ways to address this problem. Meteorological data are generally regarded as critical inputs for wind power forecasting. However, the direct use of numerical weather prediction in forecasting may not provide a high degree of accuracy due to unavoidable uncertainties, particularly for areas with complex topography. This study proposes a hybrid short-term wind power forecasting method, which integrates the corrected numerical weather prediction and spatial correlation into a Gaussian process. First, the Gaussian process model is built using the optimal combination of different kernel functions. Then, a correction model for the wind speed is designed by using an automatic relevance determination algorithm to correct the errors in the primary numerical weather prediction. Moreover, the spatial correlation of wind speed series between neighbouring wind farms is extracted to complement the input data. Finally, the modified numerical weather prediction and spatial correlation are incorporated into the hybrid model to enable reliable forecasting. The actual data in East China are used to demonstrate its performance. In comparison with the basic Gaussian process, in different seasons, the forecasting accuracy is improved by 7.02%–29.7% by using additional corrected numerical weather prediction, by 0.65–10.23% after integrating with the spatial correlation, and by 10.88–37.49% through using the proposed hybrid method.

Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes

Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one.

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Effective wind-power prediction enhances the adaptability of a wind power system to the instability of wind power, which is beneficial for load and frequency regulation, helping to convert wind power to electricity and connect wind power to the grid safely. Moreover, the use of numerical weather prediction (NWP) to predict the probability results of wind power is a matter of general concern in the field of wind power prediction, and deep neural networks have become an indispensable research tool. In this study, a new neural-network prediction model called EALSTM-QR was developed for wind-power prediction considering the input of NWP and the deep-learning method. In the model, there are four main levels: Encoder, Attention, bidirectional long short-term memory (LSTM), and quantile regression (QR). The combination inputs contain historical wind-power data and the features extracted and obtained from the NWP through the Encoder and Attention levels. The bidirectional LSTM is used to generate wind-power time-series probability prediction results. The QR method and confidence interval limits are used to obtain the final prediction intervals. The proposed method was compared with several interval prediction models and probability prediction models based on neural networks for wind-power prediction by using datasets from wind farms in China. The results indicated that the proposed EALSTM-QR has good accuracy and reliability for the prediction of intervals and probabilities.

Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

<i>Corrigendum to</i>: Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system

The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.

Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system

The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.

Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.

Smartphone Pressure Collection and Bias Correction Using Machine Learning

AbstractOver half a billion smartphones worldwide are now capable of measuring atmospheric pressure, providing a pressure network of unprecedented density and coverage. This paper describes novel approaches for the collection, quality control, and bias correction of such smartphone pressures. An Android app was developed and distributed to several thousand users, serving as a test bed for onboard pressure collection and quality-control strategies. New methods of pressure collection were evaluated, with a focus on reducing and quantifying sources of observation error and uncertainty. Using a machine learning approach, complex relationships between pressure bias and ancillary sensor data were used to predict and correct future pressure biases over a 4-week period from 10 November to 5 December 2016. This approach, in combination with simple quality-control checks, produced an 82% reduction in the average smartphone pressure bias, substantially improving the quality of smartphone pressures and facilitating their use in numerical weather prediction.

ESA’s spaceborne lidar mission ADM-Aeolus; project status and preparations for launch

ESA’s Doppler Wind lidar mission, the Atmospheric Dynamics Mission (ADM-Aeolus, hereafter abbreviated to Aeolus), was chosen as an Earth Explorer Core mission within the Living Planet Programme in 1999. It shall demonstrate the potential of space-based Doppler Wind lidars for operational measurements of wind profiles and their use in Numerical Weather Prediction (NWP) and climate research. Spin-off products are profiles of cloud and aerosol optical properties. Aeolus carries the novel Doppler Wind lidar instrument ALADIN. The mission prime is Airbus Defence & Space UK (ADS-UK), and the instrument prime is Airbus Defence & Space France (ADS-F).

ESA’s Spaceborne Lidar Mission ADM-Aeolus; Recent Achievements and Preparations for Launch

Within ESA’s Living Planet Programme, the Atmospheric Dynamics Mission (ADM-Aeolus) was chosen as the second Earth Explorer Core mission in 1999. It shall demonstrate the potential of high spectral resolution Doppler Wind lidars for operational measurements of wind profiles and their use in Numerical Weather Prediction (NWP). Spin-off products are profiles of cloud and aerosol optical properties. ADM-Aeolus carries the novel Doppler Wind lidar instrument ALADIN.Recently the two ALADIN laser transmitters were successfully qualified and delivered for further instrument integration. The instrument delivery will follow later this year and the satellite qualification and launch readiness is scheduled for 2016.In February 2015, an Aeolus Science and Calibration and Validation (CAL/VAL) Workshop was held in ESA-ESRIN, Frascati, Italy, bringing industry, the user community and ESA together to prepare for the Aeolus Commissioning and Operational Phases. During the Workshop the science, instrument and product status, commissioning phase planning and the extensive number of proposals submitted in response to the Aeolus CAL/VAL call in 2014 were presented and discussed. A special session was dedicated to the Aeolus CAL/VAL Implementation Plan.In this paper, the Aeolus mission, status and launch preparation activities are described.