Spaceborne Doppler Wind Lidar Research Articles

The impact of Aeolus winds on near-surface wind forecasts over tropical ocean and high-latitude regions

Abstract. To detect global wind profiles and improve numerical weather prediction (NWP), the European Space Agency (ESA) launched the Aeolus satellite carrying a spaceborne Doppler wind lidar in 2018. After the successful launch, the European Centre for Medium-Range Weather Forecasts (ECMWF) performed the observing system experiments (OSEs) to evaluate the contribution of Aeolus data to NWP. This study aims to assess the impact of Aeolus wind assimilation in the ECMWF model on near-surface (10 m height) wind forecasts over tropical ocean regions by taking buoy measurements for reference and over high-latitude regions by taking weather station data for reference for the year 2020. The assessments were conducted mainly through inter-comparison analysis. The results show that Aeolus data assimilation has a limited impact on sea surface wind forecasts for tropical regions when compared with buoy measurements. For the high-latitude regions in the Northern Hemisphere, Aeolus is able to improve near-surface wind forecasts. This positive impact is more evident as the forecast time step is extended, during the first half year of 2020 and during the winter months. In addition, the v component tends to benefit more from the Aeolus observations than the u component. For the Southern Hemisphere, a few error reductions are observed but exist randomly. Overall, this in situ data-based assessment expands our understanding of the role of Aeolus data assimilation with the global NWP model in predicting near-surface wind for tropical oceans and high-latitude regions.

Validation of the Aeolus Level-2B wind product over Northern Canada and the Arctic

Abstract. In August 2018, the European Space Agency (ESA) launched the Aeolus satellite, whose Atmospheric LAser Doppler INstrument (ALADIN) is the first space-borne Doppler wind lidar to regularly measure vertical profiles of horizontal line-of-sight (HLOS) winds with global sampling. This mission is intended to assess improvement to numerical weather prediction provided by wind observations in regions poorly constrained by atmospheric mass, such as the tropics, but also, potentially, in polar regions such as the Arctic where direct wind observations are especially sparse. There remain gaps in the evaluation of the Aeolus products over the Arctic region, which is the focus of this contribution. Here, an assessment of the Aeolus Level-2B (L2B) wind product is carried out, progressing from specific locations in the Canadian North to the pan-Arctic. In particular, Aeolus data are compared to a limited sample of coincident ground-based Ka-band radar measurements at Iqaluit, Nunavut, to a larger set of coincident radiosonde measurements over the Canadian North, to Environment and Climate Change Canada (ECCC)'s short-range forecast, and to the reanalysis product, ERA5, from the European Centre for Medium-Range Weather Forecasts (ECMWF). Periods covered include the early phase of the first laser flight model (flight model A – FM-A; September to October 2018), the early phase of the second laser flight model (flight model B – FM-B; August to September 2019), and the middle phase of FM-B (December 2019 to January 2020). The adjusted r-squared between Aeolus and other local datasets is around 0.9 except for lower values for the comparison to the Ka-band radar, reflecting limited sampling opportunities with the radar data. This consistency is degraded by about 10 % for the Rayleigh winds in the summer due to solar background noise and other possible errors. Over the pan-Arctic, consistency, with correlation greater than 0.8, is found in the Mie channel from the planetary boundary layer to the lower stratosphere (near surface to 16 km a.g.l.) and in the Rayleigh channel from the troposphere to the stratosphere (2 to 25 km a.g.l.). In all three periods, Aeolus standard deviations are found to be 5 % to 40 % greater than those from ECCC-B and ERA5. We found that the L2B estimated error product for Aeolus is coherent with the differences between Aeolus and the other datasets and can be used as a guide for expected consistency. Our work shows that the high quality of the Aeolus dataset that has been demonstrated globally applies to the sparsely sampled Arctic region. It also demonstrates the lack of available independent wind measurements in the Canadian North, lending urgency to the need to augment the observing capacity in this region to ensure suitable calibration and validation of future space-borne Doppler wind lidar (DWL) missions.

Comparison of Lower Stratosphere Wind Observations From the USTC's Rayleigh Doppler Lidar and the ESA's Satellite Mission Aeolus

AbstractSince the first spaceborne Doppler wind lidar, Aeolus, was launched, global wind field observations from space have been possible. Several ground‐ and air‐based validations followed, although most of these comparisons remained below 10 km in the troposphere, with rare validation work for the stratosphere. The Rayleigh Doppler lidar developed by the University of Science and Technology of China (USTC) was deployed in Xinjiang, China, in 2017. It can observe wind speed and temperature in the stratosphere and mesosphere. By setting two geographical ranges centered on the USTC lidar, the Aeolus Rayleigh winds within these ranges can be compared with ground‐based lidar wind data. Furthermore, after eliminating the effect of particulate backscatter on the USTC lidar, the lower limit of the detection range was extended to 10 km to obtain more samples. The mean biases between the Aeolus winds and the USTC lidar winds were 1.05 ± 5.98 (−0.35 ± 4.78) m/s, 1.80 ± 6.30 (−1.88 ± 4.97) m/s, and 0.17 ± 5.45 (0.51 ± 4.44) m/s for all data, ascending orbits, and descending orbits, respectively, in a large (small) geographical range. The geographical distance between the USTC lidar and the Aeolus observation swaths has a significant effect on the wind bias, the farther the distance between Aeolus observation swaths and the USTC lidar, the greater the bias. Whereas, the wind biases have no significant dependency on the time difference of the USTC lidar observation to Aeolus overflight time. Overall, the Aeolus winds are consistent with the USTC lidar winds in the stratosphere.

A statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight winds and NOAA's Global Forecast System

Abstract. The European Space Agency Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B (B10) horizontal line-of-sight (HLOS) winds, significant systematic differences between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent systematic differences between the Aeolus HLOS winds and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6 h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large, well-defined, speed-dependent systematic differences are found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Correction of these systematic differences improves the forecast impact of Aeolus data assimilated into the NOAA global NWP system.

A review of the technical system of spaceborne Doppler wind lidar and its assessment method

A review of the technical system of spaceborne Doppler wind lidar and its assessment method

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

AbstractAeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line‐of‐sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level‐2B (L2B) wind statistics have been assessed using the European Centre for Medium‐range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0–7.0 m·s−1 for the Rayleigh‐clear and 2.8–3.6 m·s−1 for the Mie‐cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms. Complex systematic HLOS wind error variations on time‐scales less than one orbit were identified, most strongly affecting the Rayleigh‐clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020. In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short‐range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root‐mean‐square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere. Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh‐clear and Mie‐cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh‐clear and Mie‐cloudy winds providing similar overall short‐range impact in 2020.

Relationship between wind observation accuracy and the ascending node of the sun-synchronous orbit for the Aeolus-type spaceborne Doppler wind lidar

Abstract. The launch and operation of the first spaceborne Doppler wind lidar (DWL), Aeolus, is of great significance for observing the global wind field. Aeolus operates on a sun-synchronous dawn–dusk orbit to minimize the negative impact of solar background radiation (SBR) on wind observation accuracy. Future spaceborne DWLs may not operate on sun-synchronous dawn–dusk orbits due to their observational purposes. The impact of the local time of ascending node (LTAN) crossing of sun-synchronous orbits on the wind observation accuracy was studied in this paper by proposing two given Aeolus-type spaceborne DWLs operating on the sun-synchronous orbits with LTANs of 15:00 and 12:00 LT. On these two new orbits, the increments of the averaged SBR received by the new spaceborne DWLs range from 39 to 56 mW m−2 sr−1 nm−1 under cloud-free skies near the summer and winter solstices, which will lead to uncertainties of 0.19 and 0.27 m s−1 in the increment of the averaged Rayleigh channel wind observations for 15:00 and 12:00 LT orbits using the instrument parameters of Aeolus with 30 measurements per observation and 20 laser pulses per measurement. This demonstrates that Aeolus operating on the sun-synchronous dawn–dusk orbit is the optimal observation scenario, and the random error caused by the SBR will be larger on other sun-synchronous orbits. Increasing the laser pulse energy of the new spaceborne DWLs is used to lower the wind observation uncertainties, and a method to quantitatively design the laser pulse energy according to the specific accuracy requirements is proposed in this study based on the relationship between the signal-to-noise ratio and the uncertainty of the response function of the Rayleigh channel. The laser pulse energies of the two new spaceborne DWLs should be set to 70 mJ based on the statistical results obtained using the method. The other instrument parameters should be the same as those of Aeolus. Based on the proposed parameters, the accuracies of about 77.19 % and 74.71 % of the bins of the two new spaceborne DWLs would meet the accuracy requirements of the European Space Agency (ESA) for Aeolus. These values are very close to the 76.46 % accuracy of an Aeolus-type spaceborne DWL when it is free of the impact of the SBR. Moreover, the averaged uncertainties of the two new spaceborne DWLs are 2.62 and 2.69 m s−1, which perform better than that of Aeolus (2.77 m s−1).

Airborne lidar observations of wind, water vapor, and aerosol profiles during the NASA Aeolus calibration and validation (Cal/Val) test flight campaign

Abstract. Lidars are uniquely capable of collecting high-precision and high spatiotemporal resolution observations that have been used for atmospheric process studies from the ground, aircraft, and space for many years. The Aeolus mission, the first space-borne Doppler wind lidar, was developed by the European Space Agency (ESA) and launched in August 2018. Its novel Atmospheric LAser Doppler INstrument (ALADIN) observes profiles of the component of the wind vector and aerosol/cloud optical properties along the instrument's line-of-sight (LOS) direction on a global scale. A total of two airborne lidar systems have been developed at NASA Langley Research Center in recent years that collect measurements in support of several NASA Earth Science Division focus areas. The coherent Doppler Aerosol WiNd (DAWN) lidar measures vertical profiles of LOS velocity along selected azimuth angles that are combined to derive profiles of horizontal wind speed and direction. The High Altitude Lidar Observatory (HALO) measures high resolution profiles of atmospheric water vapor (WV) and aerosol and cloud optical properties. Because there are limitations in terms of spatial and vertical detail and measurement precision that can be accomplished from space, airborne remote sensing observations like those from DAWN and HALO are required to fill these observational gaps and to calibrate and validate space-borne measurements. Over a 2-week period in April 2019, during their Aeolus Cal/Val Test Flight campaign, NASA conducted five research flights over the eastern Pacific Ocean with the DC-8 aircraft. The purpose was to demonstrate the following: (1) DAWN and HALO measurement capabilities across a range of atmospheric conditions, (2) Aeolus Cal/Val flight strategies and comparisons of DAWN and HALO measurements with Aeolus, to gain an initial perspective of Aeolus performance, and (3) ways in which atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, WV, and aerosol profile observations, coupled with numerical model and other remote sensing observations. This paper provides a brief description of the DAWN and HALO instruments, discusses the synergistic observations collected across a wide range of atmospheric conditions sampled during the DC-8 flights, and gives a brief summary of the validation of DAWN, HALO, and Aeolus observations and comparisons.

Aeolus L2 Activities at KNMI

KNMI has had a long involvement with the recently launched Aeolus mission. Aeolus is an ESA Earth-Explorer mission and embarks the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN is the first space-borne Doppler Wind Lidar (DWL) and was successfully launched in late August 2018. KNMI has had a long association with this mission and is sharing the software development of the L2B processor used to generate L2B wind products at ECMWF, develops related scene classification algorithms and performs calibration/validation activities. In addition, KNMI conducts more fundamental work for improving the retrieval of atmosphere optical properties, taking into account multiple scattering effects.

Design and experimental verification of a novel Mie Doppler wind lidar based on all-fiber Mach-Zehnder frequency discriminator

Spaceborne Doppler wind lidar is currently one of the hot spots on the lidar technology. The all-fiber Mach-Zehnder interferometer (FMZI) as a frequency discriminator of Doppler wind lidar is proposed for profiling the atmospheric wind velocity. The frequency discriminator system parameters are optimized, and the retrieval method of wind velocity based on FMZI is deduced. The arm length difference of FMZI for the aerosol backscattering signal is optimized to be 74.8cm at the laser wavelength of 532nm. The maximum system sensitivity for wind profiling can reach up to 2.62%/(m/s), and the dynamic range of wind velocity is ±18.2m/s. The system simulation shows that the detection range is up to 6.7km for 1m/s wind velocity error at a wind velocity of 15m/s with laser energy of 250mJ and telescope diameter of 406mm. A rotating disc experimental system is designed to simulate the atmospheric wind field for verifying the feasibility of the system, and the results show that there is good agreement between the retrieved wind velocity and simulated wind velocity. The simulation and experimental test results show that FMZI is feasible as a frequency discriminator and can be suitable for direct Mie Doppler lidar, especially for satellite-based platform lidar due to its desirable characteristics, including its small volume, light weight, good stability and compact structure.

Simulation of frequency discrimination for spaceborne Doppler wind lidar (I)：Study on the retrieval of atmospheric wind speed for Mie channel based on Fizeau interferometer

System structure of spaceborne Doppler wind lidar is investigated. Simulation system of frequency discrimination is built based on Fizeau interferometer. The wind retrieval algorithm of Mie channel is simulated and studied. The horizontal line-of-sight (HLOS) wind error is analysed using the simulated result of the radiosonde dataset. The result shows that the wind speed of lower troposphere can be retrieved in Mie channel based on Fizeau interferometer. The average values of HLOS wind error deviation and standard deviation in lower troposphere are less than 1 m·s-1 and 2 m·s-1, respectively. Distributions of aerosol and cloud have an influence on wind error for spaceborne Doppler wind lidar, and the maximum wind deviation will increase twice.

Simulation of frequency discrimination for spaceborne Doppler wind lidar (Ⅱ)：Study on the retrieval of atmospheric wind speed for Rayleigh channel based on Fabry-Perot interferometer

Based on the principle of spaceborne Doppler wind lidar, a simulation system of frequency discrimination is built based on the double sequential Fabry-Perot (F-P) interferometer. The wind retrieval algorithm of Rayleigh channel is simulated and studied. The influence on the retrieved atmospheric line-of-sight (LOS) wind speed in Rayleigh channel by the Rayleigh-Brillouin effect and Mie contamination is systematically analyzed. The horizontal line-of-sight (HLOS) wind error is analysed using the simulated result of the radiosonde dataset. The results show that the wind speeds of the middle and upper atmosphere can be retrieved in Rayleigh channel based on the double sequential F-P interferometer; the Rayleigh-Brillouin effect and Mie contamination influence the accuracy of LOS wind speed retrieval in Rayleigh channel; the Rayleigh channel requires more accurate temperature; Mie contamination can be ignored in clear atmosphere; when Brillouin effect is not considered, below 2 km, the HLOS wind speed cannot be retrieved in Rayleigh channel, and above 2 km, the HLOS wind speed error in Rayleigh channel is less than 0.4 m/s and its standard deviation is 1-4 m/s. Just as the Mie channel, distributions of aerosol and cloud have an influence on wind error for spaceborne Doppler wind lidar in Rayleigh channel. The research results have an important reference value for the development of spaceborne lidar wind technology.

Impact Assessment of Simulated Doppler Wind Lidars with a Multivariate Variational Assimilation in the Tropics

Abstract This paper deals with the dynamical aspect of variational data assimilation in the tropics and the role of the background-error covariances in the observing system simulation experiments for the tropics. The study uses a model that describes the horizontal structure of the potential temperature and wind fields in regions of deep tropical convection. The assimilation method is three- and four-dimensional variational data assimilation. The background-error covariance model for the assimilation is a multivariate model that includes the mass–wind couplings representative of equatorial inertio-gravity modes and equatorial Kelvin and mixed Rossby–gravity modes in addition to those representative of balanced equatorial Rossby waves. Spectra of the background errors based on these waves are derived from the tropical forecast errors of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Tropical mass–wind (im)balances are illustrated by studying the potential impact of the spaceborne Doppler wind lidar (DWL) Atmospheric Dynamic Mission (ADM)-Aeolus, which measures horizontal line-of-sight (LOS) wind components. Several scenarios with two DWLs of ADM-Aeolus type are compared under different flow conditions and using different assumptions about the quality of the background-error covariances. Results of three-dimensional variational data assimilation (3DVAR) illustrate the inefficiency of multivariate assimilation in the tropics. The consequence for the assimilation of LOS winds is that the missing part of the wind vector can hardly be reconstructed from the mass-field observations and applied balances as in the case of the midlatitudes. Results of four-dimensional variational data assimilation (4DVAR) show that for large-scale tropical conditions and using reliable background-error statistics, differences among various DWL scenarios are not large. As the background-error covariances becomes less reliable, horizontal scales become smaller and the flow becomes less zonal, the importance of obtaining information about the wind vector increases. The added value of another DWL satellite increases as the quality of the background-error covariances deteriorates and it can be more than twice as large as in the case of reliable covariances.

A cycled sensitivity observing system experiment on simulated Doppler wind lidar data during the 1999 Christmas storm ‘Martin’

In a companion paper in this issue Sensitivity Observing System Experiment (SOSE) has been introduced as a new method to assess the potential added value of future observing systems for Numerical Weather Prediction (NWP). There, SOSE was introduced as a single cycle experiment, meaning that additional synthetic observations, to extend the existing global observing system (GOS), are applied in only one assimilation cycle. In this study SOSE has been extended to run over three subsequent days to enable impact assessment of additional prospective observations over a prolonged period prior to an event. This is achieved by a cycled implementation of the SOSE method where analysis adaptations from previous cycles evolve progressively in subsequent cycles. This implementation of a cycled SOSE results in a sequence of pseudo-true atmospheric states that are subsequently used for the simulation of prospective extensions of the existing GOS. A cycled SOSE has two attractive properties as compared to the single-cycle implementation (i) the resulting pseudo-true atmospheric state at the end of the cycling period, that is, at forecast initial time, provides a better forecast and (ii) the cycling implementation makes the SOSE method more suitable for absolute impact assessment of continuously operated observing systems such as from polar satellite platforms. The NWP case investigated concerns the 1999 Christmas storm ‘Martin’ that caused much havoc in Western Europe. We show that additional observations from a spaceborne Doppler wind lidar over a 3-d period would have improved the 2-d forecast of ‘Martin’ substantially. This is substantiated by a 50-member ensemble forecast run.

A cycled sensitivity observing system experiment on simulated Doppler wind lidar data during the 1999 Christmas storm ‘Martin’

In a companion paper in this issue Sensitivity Observing System Experiment (SOSE) has been introduced as a new method to assess the potential added value of future observing systems for NumericalWeather Prediction (NWP). There, SOSEwas introduced as a single cycle experiment, meaning that additional synthetic observations, to extend the existing global observing system (GOS), are applied in only one assimilation cycle. In this study SOSE has been extended to run over three subsequent days to enable impact assessment of additional prospective observations over a prolonged period prior to an event. This is achieved by a cycled implementation of the SOSE method where analysis adaptations from previous cycles evolve progressively in subsequent cycles. This implementation of a cycled SOSE results in a sequence of pseudo-true atmospheric states that are subsequently used for the simulation of prospective extensions of the existing GOS. A cycled SOSE has two attractive properties as compared to the single-cycle implementation (i) the resulting pseudo-true atmospheric state at the end of the cycling period, that is, at forecast initial time, provides a better forecast and (ii) the cycling implementation makes the SOSE method more suitable for absolute impact assessment of continuously operated observing systems such as from polar satellite platforms. The NWP case investigated concerns the 1999 Christmas storm ‘Martin’ that caused much havoc in Western Europe. We show that additional observations from a spaceborne Doppler wind lidar over a 3-d period would have improved the 2-d forecast of ‘Martin’ substantially. This is substantiated by a 50-member ensemble forecast run.

Sensitivity Observing System Experiment (SOSE)-a new effective NWP-based tool in designing the global observing system

Lacking an established methodology to test the potential impact of prospective extensions to the global observing system (GOS) in real atmospheric cases we developed such a method, called Sensitivity Observing System Experiment (SOSE). For example, since the GOS is non uniform it is of interest to investigate the benefit of complementary observing systems filling its gaps. In a SOSE adjoint sensitivity structures are used to define a pseudo true atmospheric state for the simulation of the prospective observing system. Next, the synthetic observations are used together with real observations from the existing GOS in a state-of-the-art Numerical Weather Prediction (NWP) model to assess the potential added value of the new observing system. Unlike full observing system simulation experiments (OSSE), SOSE can be applied to real extreme events that were badly forecast operationally and only requires the simulation of the new instrument. As such SOSE is an effective tool, for example, to define observation requirements for extensions to the GOS. These observation requirements may serve as input for the design of an operational network of prospective observing systems. In a companion paper we use SOSE to simulate potential future space borne DopplerWind Lidar (DWL) scenarios and assess their capability to sample meteorologically sensitive areas not well captured by the current GOS, in particular over the Northern Hemisphere oceans.

The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis

Abstract Through the use of observation operators, modern data assimilation systems have the capability to ingest observations of quantities that are not themselves model variables but are mathematically related to those variables. An example of this is the so-called line-of-sight (LOS) winds that a spaceborne Doppler wind lidar (DWL) instrument would provide. The model or data assimilation system ideally would need information about both components of the horizontal wind vectors, whereas the observations in this case would provide only the projection of the wind vector onto a given direction. The estimated or analyzed value is then calculated essentially as a weighted average of the observation itself and the model-simulated value of the observed quantity. To assess the expected impact of a DWL, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. The answer to this question depends on the fundamental character of the atmospheric flow fields that are analyzed, but, just as important, it also depends on the real and assumed error covariance characteristics of these fields. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial distribution and error characteristics and about the error covariance of the background fields. The basic design of the system and experimental results obtained with it are presented. The experiments were designed to illustrate how such a system may be used in the instrument concept definition phase.

Assimilation of Equatorial Waves by Line-of-Sight Wind Observations

This paper investigates the potential of line-of-sight (LOS) wind information from a spaceborne Doppler wind lidar to reduce uncertainties in the analysis fields of equatorial waves. The benefit of LOS winds is assessed by comparing their impact to that of a single wind component, full wind field information, and mass field data in three- and four-dimensional variational data assimilation. The dynamical framework consists of nonlinear shallow-water equations solved in spectral space and a background error term based on eigenmodes derived from linear equatorial wave theory. Based on observational evidence, simulated wave motion fields contain equatorial Kelvin, Rossby, mixed Rossby–gravity, and the lowest two modes of the westward-propagating inertio–gravity waves. The same dynamical structures are included, entirely or partially, into the background error covariance matrix for the multivariate analysis. The relative usefulness of LOS data is evaluated by carrying out “identical twin” observing system simulation experiments and assuming a perfect model. Results from the experiments involving a single observation or an imperfect background error covariance matrix illustrate that the assimilation increments due to LOS wind information rely more on the background error term specification than the full wind field information. This sensitivity is furthermore transferred to the balanced height field increments. However, all assimilation experiments suggest that LOS wind observations have a capability of being valuable and need supplemental information to the existing satellite mass field measurements in the Tropics. Although the new wind information is incomplete, it has a potential to provide reliable analysis of tropical wave motions when it is used together with the height data.

Development and potential of space-borne doppler wind lidar

The advantage of lidar over other wind sensors is presented in this paper. With more than 20 years research, the development of the space-borne wind lidar is reviewed. Longer-term investigation has made many technologies suitable for the wind lidar measurement from an orbital platform become mature. However, there are still some problems to be solved. In order to obtain the optimal performance in wind detection, great importance is being attached to the simulation of a virtual space-borne wind lidar system on computer as developed by NASA and ESA.

Impact of profile observations on the German Weather Service's NWP system

In preparation for a study on the potential impact of a space-borne Doppler wind lidar on the quality of NWP products, a series of assimilations and forecasts were conducted to estimate the potential benefit of conventional wind and temperature profile measurements over North America to numerical weather forecasts for the Northern Hemisphere and specifically, Europe. A comparison of the forecast quality of a control run, using all available observations, to experiments omitting wind and temperature data from specific instruments (radiosondes, pilot stations and aircraft) makes it possible to estimate the importance of the omitted data, and clarify whether winds derived from the geostrophic relation are sufficient or whether observed wind profiles result in a more realistic definition of the initial state for numerical weather prediction systems in the extratropic regions. Very little impact on forecast quality was noted when wind or temperature observations from radiosondes and pilots were excluded from the assimilation process. However, a clear deterioration in forecast quality was observed when additionally all available wind or temperature measurements from aircraft were also withheld. Comparisons of the relative utility of wind and temperature observations over North America show that assimilations and forecasts derive more benefit from wind data than from temperature data. The greatest deterioration could be observed if both wind and temperature observations were omitted from the assimilation cycle. By tracing the differences between the control forecasts and the experimental forecasts to their initial difference, the regions around Hudson Bay. Novia Scotia, Buffin Bay and Northern Canada could be identified as sensitive areas, i.e. those where a missing observation could have a substantial effect on the forecast for the Northern Hemisphere and Europe. Comparisons of the relative utility of radiosonde wind and temperature observations over Canada and Alaska to numerical weather forecast quality, in contrast to the sonde and aircraft network over the United States, reveal the importance of the conventional radiosonde network in the higher northern latitudes.