Wind Retrieval Research Articles

A Physics-Aware Neural Network Approach for Flow Data Reconstruction From Satellite Observations

An accurate assessment of physical transport requires high-resolution and high-quality velocity information. In satellite-based wind retrievals, the accuracy is impaired due to noise while the maximal observable resolution is bounded by the sensors. The reconstruction of a continuous velocity field is important to assess transport characteristics and it is very challenging. A major difficulty is ambiguity, since the lack of visible clouds results in missing information and multiple velocity fields will explain the same sparse observations. It is, therefore, necessary to regularize the reconstruction, which would typically be done by hand-crafting priors on the smoothness of the signal or on the divergence of the resulting flow. However, the regularizers can smooth the solution excessively and will not guarantee that possible solutions are truly physically realizable. In this paper, we demonstrate that data recovery can be learned by a neural network from numerical simulations of physically realizable fluid flows, which can be seen as a data-driven regularization. We show that the learning-based reconstruction is especially powerful in handling large areas of missing or occluded data, outperforming traditional models for data recovery. We quantitatively evaluate our method on numerically-simulated flows, and additionally apply it to a Guadalupe Island case study—a real-world flow data set retrieved from satellite imagery of stratocumulus clouds.

Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds

AbstractDirect remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as ‘Gradient Feature’ (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller (10−4 s−1) than the finer ones (10−3 s−1). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed.

Sea Surface Wind Retrieval Using the Combined Scatterometer and Altimeter Backscatter Measurements of the HY-2B Satellite

Sea surface winds derived from the spaceborne rotating pencil-beam scatterometers, such as the current Chinese Haiyang-2 (HY-2) scatterometers and the Indian SCATSAT-1 scatterometer, are used in a wide range of atmospheric and oceanic applications. One key problem of the pencil-beam scatterometers is the relatively low wind quality in the nadir region, due to the poor azimuth diversity of the nadir-track observations. This article proposes to include the altimeter backscatter measurements in the wind inversion to improve the retrieved wind quality in the nadir region of HY-2B scatterometer (HSCAT). First, empirical model functions are developed to relate the C- and Ku-band altimeter backscatters to the neutral surface winds at 10-m height. Subsequently, the maximum likelihood estimator, which is commonly used to perform wind inversion for satellite scatterometers, is adapted to retrieve winds with simultaneous scatterometer and altimeter measurements. The significance of altimeter measurements in the combined wind inversion is addressed by comparing the cost functions of the combined scatterometer/altimeter wind retrieval and the nominal scatterometer inversion. The experimental results are eventually validated using moored buoy winds and the European Centre for Medium-range Weather Forecasting (ECMWF) model winds, indicating that the inclusion of altimeter backscatters has a positive impact on the wind quality over the HSCAT nadir region. Finally, the potential approaches to further improve the combined scatterometer and altimeter wind inversion are summarized.

Twenty-Seven Years of Scatterometer Surface Wind Analysis over Eastern Boundary Upwelling Systems

More than twelve satellite scatterometers have operated since 1992 through the present, providing the main source of surface wind vector observations over global oceans. In this study, these scatterometer winds are used in combination with radiometers and synthetic aperture radars (SAR) for the better determination and characterization of high spatial and temporal resolution of regional surface wind parameters, including wind speed and direction, wind stress components, wind stress curl, and divergence. In this paper, a 27-year-long (1992–2018) 6-h satellite wind analysis with a spatial resolution of 0.125° in latitude and longitude is calculated using spatial structure functions derived from high-resolution SAR data. The main objective is to improve regional winds over three major upwelling regions (the Canary, Benguela, and California regions) through the use of accurate and homogenized wind observations and region-specific spatial and temporal wind variation structure functions derived from buoy and SAR data. The long time series of satellite wind analysis over the California upwelling, where a significant number of moorings is available, are used for assessing the accuracy of the analysis. The latter is close to scatterometer wind retrieval accuracy. This assessment shows that the root mean square difference between collocated 6-h satellite wind analysis and buoys is lower than 1.50 and 1.80 m s−1 for offshore and nearshore locations, respectively. The temporal correlation between buoy and satellite analysis winds exceeds 0.90. The analysis accuracy is lower for 1992–1999 when satellite winds were mostly retrieved from ERS-1 and/or ERS-2 scatterometers. To further assess the improvement brought by this new wind analysis, its data and data from three independent products (ERA5, CMEMS, and CCMP) are compared with purely scatterometer winds over the Canary and Benguela regions. Even though the four products are generally similar, the new satellite analysis shows significant improvements, particularly in the upwelling areas.

Assimilation of lidar planetary boundary layer height observations

Abstract. Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22-hourly forecasts from the NASA Unified – Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection at Night (PECAN) campaign on 11 July 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with six independent radiosonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had a small impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the Doppler lidar PBLH observations was found to improve the temperature and V-velocity profiles relative to independent radiosonde profiles. Water vapor was overcorrected, leading to increased differences with independent data. Errors in the U velocity were made slightly larger. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.

Analysis of the High-Latitude Sea Surface Wind Acquisition Ability of Seven Satellite Scatterometers

Seven satellite scatterometers, namely, the C-band MetOp-A/Advanced Scatterometer (ASCAT), MetOp-B/ASCAT, MetOp-C/ASCAT, Ku-band HY-2A/SCAT, HY-2B/SCAT, Chinese-French Oceanography Satellite (CFOSAT)/SCAT, and SCATSAT-1/OceanSat Satellite Scatterometer (OSCAT2), are operating in orbits. These satellites make a high-frequency observation of sea surface winds possible, particularly at high latitudes. This work analyzes the passing time, passing frequency, coverage of these seven scatterometers, and the quality of high-latitude surface wind products. All the scatterometer wind products were reproduced with the same processing procedures, in terms of backscatter calibration, wind retrieval, numerical weather prediction (NWP) wind, and quality control. The target region (74–78 N, 167°–171 W) was chosen to analyze the passing frequency. A spatial grid of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.25^{\circ } \times 0$ </tex-math></inline-formula> .25° in the range of 60° N–88° N was chosen to analyze the spatial coverage of the scatterometers. The results show that more than 40 observation times can be provided daily by all the seven scatterometers; however, there is a 9-h gap between UTC 9:00 and UTC 18:00, suggesting that international cooperation is needed for optimizing the equatorial crossing time in future scatterometer missions, such that an optimal virtual scatterometer constellation can be achieved. The scatterometer winds are compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) winds, and with each other. The comparison results confirmed noticeable wind speed biases due to sea surface temperature (SST) in all Ku-band scatterometer winds with respect to the C-band scatterometer and ERA5 winds.

The evaluation of damped Newton variational ocean wind retrieval method

This paper researches on Synthetic Aperture Radar (SAR) ocean wind retrieval approach, evaluates the method of Damped Newton VARiational method (DNVAR) and the direct method (DIRECT) using two years’ real SAR data and different background winds. Experiments are carried out using SENTINEL-1 SAR information as observation, National Data Buoy Center (NDBC) buoy data as in-situ data, Global Forecast System (GFS), Advanced SCATterometer (ASCAT), WindSAT, and Cross-Calibrated Multi-Platform (CCMP) wind products as background data. Thousands of match-ups are extracted. Results show that DNVAR can improve the wind field accuracy when the background data is ASCAT and CCMP. Especially, DNVAR wind speed results are much better than the official product of SENTINEL-1 wind data. The background data is crucial to the wind field retrieval results, so high accuracy of background data could lead to high accuracy results.

Reducing Errors in Velocity–Azimuth Display (VAD) Wind and Deformation Retrievals from Airborne Doppler Radars in Convective Environments

AbstractThe present study describes methods to reduce the uncertainty of velocity–azimuth display (VAD) wind and deformation retrievals from downward-pointing, conically scanning, airborne Doppler radars. These retrievals have important applications in data assimilation and real-time data processing. Several error sources for VAD retrievals are considered here, including violations to the underlying wind field assumptions, Doppler velocity noise, data gaps, temporal variability, and the spatial weighting function of the VAD retrieval. Specific to airborne VAD retrievals, we also consider errors produced due to the radar scans occurring while the instrument platform is in motion. While VAD retrievals are typically performed using data from a single antenna revolution, other strategies for selecting data can be used to reduce retrieval errors. Four such data selection strategies for airborne VAD retrievals are evaluated here with respect to their effects on the errors. These methods are evaluated using the second hurricane nature run numerical simulation, analytic wind fields, and observed Doppler radar radial velocities. The proposed methods are shown to reduce the median absolute error of the VAD wind retrievals, especially in the vicinity of deep convection embedded in stratiform precipitation. The median absolute error due to wind field assumption violations for the along-track and for the across-track wind is reduced from 0.36 to 0.08 m s−1 and from 0.35 to 0.24 m s−1, respectively. Although the study focuses on Doppler radars, the results are equally applicable to conically scanning Doppler lidars as well.

Variational Retrievals of High Winds Using Uncalibrated CyGNSS Observables

This study presents a new retrieval approach for obtaining wind speeds from CyGNSS level-1 observables. Unlike other existing approaches, (1) this one is a variational technique that is based on a physical forward model, (2) it uses uncalibrated bin raw counts observables, (3) the geophysical information content comes from only one pixel of the broader delay-Doppler map, finest achievable resolution in level-1 products over the sea, and (4) calibrates them against track-wise polynomial adjustments to a background numerical weather prediction model. Through comparisons with the background model, other spaceborne sensors (SMAP, SMOS, ASCAT-A/B), and CyGNSS wind retrievals by other organizations, the study shows that this approach has skills to infer wind speeds, including hurricane force winds. For example, the Pearson’s correlation coefficient between these CyGNSS retrievals and ERA5 is 0.884, 0.832 with NOAA CyGNSS results, and 0.831 with respect to SMAP co-located measurements. Furthermore, the variational retrieval algorithm is a simplified version of the more general equations that are used in data assimilation, and the calibration scheme could also be integrated in the assimilation process. Therefore, this approach is also a good tool for analyzing the potential performance of ingesting uncalibrated level-1 single-pixel observables into NWP.

A note on radar signatures of hydrometeors in the melting layer as inferred from Sentinel-1 SAR data acquired over the ocean

Synthetic aperture radar (SAR) images acquired over the ocean often show radar signatures of rain, which are not easy to interpret. The scattering mechanisms causing radar signatures are usually attributed to surface scattering due to sea surface roughness variations caused by raindrops impinging onto the sea surface and/or by up- and downdraft winds. In this paper, we address another radar signature of rain, which is often observed in C-band (and also in X-band) SAR images, but whose origin has been a matter of debate in the ocean remote sensing community since long time and has not been solved yet. This radar signature consists of areas of very high radar backscatter (bright patches) at co- as well as well as at cross-polarization. This paper aims at providing evidence that it is not caused by surface scattering, but by volume scattering from wobbling, non-spherical, oblate hydrometeors within the melting layer. To this end, we first review the theory of radar backscattering from the melting layer as developed by D'Amico et al. (1998) and then present historic radar backscatter data from the melting layer carried out by ground-based and airborne radars, which validate this theory. Then we show four representative Sentinel-1 SAR images acquired over the sea area close to Hong Kong and a SIR-C/X-SAR image acquired over the Gulf of Mexico, which show pronounced radar signatures of rain (bright patches) at co-polarization (VV) and cross-polarization (VH). The analysis of the SAR images yields the result that within the bright patches the ratio of the radar backscatter at cross-polarization to the one at co-polarization shows the same characteristics as the linear depolarization ratio (LDR) measured by radar meteorologist in radar backscattering from the melting layer. Furthermore, we show that radar signatures of rain due to volume scattering may interfere with co-polarization radar signatures of rain due to surface scattering. Thus, cross-polarization SAR images are better suited to detect radar backscattering from the melting layer than co-polarization SAR images, This investigation is of relevance for ocean surface wind retrieval using C-band SARs, since scattering at hydrometeors in the melting layer can cause significant errors in ocean wind retrieval. Areas with simultaneously high co- and cross-polarization NRCS values of around −10 dB and − 20 dB, respectively, have to be flagged as areas where the conventional wind retrieval algorithm cannot be applied.

Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval

The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on Bayesian theory can overcome the limitation of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this paper, we propose a new wind retrieval method by applying optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated wellcharacterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. Experimental results of the simulation data and Sentinel-1 SAR data show that the OI wind retrieval method can effectively reduce the background wind error and is more sensitive to wind speed than wind direction. Compared with other methods, the accuracy of the OI method is similar to that of the VAR method, but significantly higher than that of the direct wind retrieval (DIRECT) method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The OI method can be effectively applied to SAR wind retrieval and has unique advantages.Sea surface wind is a crucial parameter for studying the physical quantity of the sea surface and plays an important role in many fields such as weather forecasts [1, 2], wind energy resource management [3,4], wave numerical simulation [5,6], and oil spill monitoring [7,8]. Because of the limited spatial and temporal coverage, high-precision sea surface wind acquired from buoys, ships and offshore platforms are not meeting the growing demand [9].In recent decades, with the development of satellite remote sensing, the technology of acquiring sea surface wind using satellite sensor detection data has gradually matured and improved. Among various satellite sensors, microwave radiometers and scatterometers play an important role in providing global sea surface wind. However, microwave radiometers and scatterometers can acquire only low spatial resolution (12.5-50 km) sea surface wind. This relatively low spatial resolution is

Polar Winds: Airborne Doppler Wind Lidar Missions in the Arctic for Atmospheric Observations and Numerical Model Comparisons

During October–November 2014 and May 2015, NASA sponsored and conducted a pair of airborne campaigns called Polar Winds to investigate atmospheric circulations, particularly in the boundary layer, over the Arctic using NASA’s Doppler Aerosol WiNd (DAWN) lidar. A description of the campaigns, the DAWN instrument, wind retrieval methods and data processing is provided. During the campaigns, the DAWN instrument faced backscatter sensitivity issues in the low aerosol conditions that were fairly frequent in the 2–6 km altitude range. However, when DAWN was able to make measurements, comparisons with dropsondes show good agreement and very low bias and supports the use of an airborne Doppler wind lidar such as DAWN that can provide profiles with high velocity precision, ~65 m vertical resolution and horizontal spacing as fine as 3–7 km. Case study analyses of a Greenland tip jet, barrier winds and an upper level jet are presented and show how, despite sensitivity issues, DAWN data can be confidently used in diagnostic studies of dynamic features in the Arctic. Comparisons with both an operational and research Weather Research and Forecasting (WRF) model for these events also show the potential for utilization in model validation. The sensitivity issues of the DAWN laser have since been corrected.

First Spaceborne Version of Velocity-Azimuth Display Technique for Wind Field Retrieval on Cloud and Precipitation Radar

Cloud and precipitation radar mounted on a polar orbiting satellite opens up a new opportunity for global wind observation to improve numerical weather forecasting and prevent weather disasters. However, no related works have been done to retrieve the wind field for spaceborne cloud and precipitation radar. This is mainly because the high-speed motion of satellites makes wind field retrieval complex. This paper developed the first spaceborne version of the velocity–azimuth display (VAD) technique for wind field retrieval, which was originally created for ground-based radar. After derivation of VAD for spaceborne radar, we found that the product of the azimuth of the radar beam and its first harmonic was introduced into the Fourier series of radar radial velocity due to the motion of the satellites. The wind retrieval equations were developed by considering the effects of satellite motion and conical scanning strategy of radar. Numerical simulations of the spaceborne radar showed that the proposed VAD method provided a mean vertical profile of the horizontal wind with high vertical resolution over a large observation swath. Validations on airborne radar data with the same conical scan strategy as the spaceborne radar were carried out to capture the average wind structure in one hurricane event. The real data results demonstrated that the wind-retrieved results by the proposed method were consistent with the ground truth data, indicating the potential use of our proposal for spaceborne radar.

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA . PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence. Funding statement: The development of this software is supported by the Climate Model Development and Validation (CMDV) activity which is funded by the Office of Biological and Environmental Research in the US Department of Energy Office of Science.

Errors From Asymmetric Emission Rate in Spaceborne, Limb Sounding Doppler Interferometry: A Correction Algorithm With Application to ICON/MIGHTI.

The Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) on NASA's Ionospheric Connection Explorer (ICON) mission is designed to measure the neutral wind and temperature between 90 and 300 km altitude. Using the Doppler Asymmetric Spatial Heterodyne (DASH) spectroscopy technique, observations from MIGHTI can be used to derive thermospheric winds by measuring Doppler shifts of the atomic oxygen red line (630.0 nm) and green line (557.7 nm). Harding et al. (2017, https://doi.org/10.1007/s11214‐017‐0359‐3) (Harding17) describe the wind retrieval algorithm in detail and point out the large uncertainties that result near the solar terminators and equatorial arcs, regions of large spatial gradients in airglow volume emission rates (VER). The uncertainties originate from the assumption of a constant VER at every given altitude, resulting in errors where the assumption is not valid when limb sounders, such as MIGHTI, observe regions with significant VER gradients. In this work, we introduce a new wind retrieval algorithm (Wu20) with the ability to account for VER that is asymmetric along the line of sight with respect to the tangent point. Using the predicted ICON orbit and simulated global VER variation, the greatest impact of the symmetric airglow assumption to the ICON vector wind product is found within 30° from the terminator when the spacecraft is in the dayside, causing an error of at least 10 m/s. The new algorithm developed in this study reduces the error near the terminator by a factor of 10. Although Wu20 improves the accuracy of the retrievals, it loses precision by 75% compared to Harding17.

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Abstract. We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from −0.8 to 0.6 m s−1. Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m s−1, while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s−1. The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s−1 for SAR and less than 0.2 m s−1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

Effects of Temperature on Sea Surface Radar Backscattering Under Neutral and Nonneutral Atmospheric Conditions for Wind Retrieval Applications: A Numerical Study

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

Development of a wind retrieval method for low-speed low-pressure flows for ExoMars

Forced convective heat transfer from three horizontally inclined rectangular-based cylinders (rods) has been studied experimentally under representative Martian near-surface air flows in the Aarhus Wind Tunnel Simulator (AWTS), Denmark. The testing campaign was developed for the HABIT (Habitability: Brines, Irradiation and Temperature) instrument, European payload on board the ExoMars 2022 Kazachok surface platform. The average heat transfer coefficient was determined from steady CO2 flows at a pressure of 9.9 mbar, an ambient temperature of ∼25 °C, and for horizontal free-stream velocities between 0.8 and 12 m/s. A retrieval algorithm to derive the wind speed from the average heat transfer coefficient estimated at each of the three HABIT Air Temperature Sensors (ATS) rods was calibrated within the AWTS. The ATS rods are placed one at the front of the instrument structure (ATS2) and two on the sides (ATS1 and ATS3); and under Martian atmospheric conditions these rods serve as cooling fins. Several relationships between the Nusselt number and the Reynolds and Prandtl numbers reported in the literature were evaluated to model convective heat transfer from the ATS rods. Where needed, corrections to account for radiative heat transfer within the AWTS were implemented. The final retrieval method demonstrated that wind speed can be retrieved for frontal winds in the range of 0–10 m/s, with an error of ±0.3 m/s, using the cooling profile of the ATS rod 3, and for lateral winds in the range of 0–6 m/s, with an error of ±0.3 m/s, using the ATS rod 2 cooling profile.

Three-dimensional wind profiles using a stabilized shipborne cloud radar in wind profiler mode

Abstract. In this study, a shipborne 95 GHz Doppler cloud radar mounted on a stabilized platform is used to retrieve vertical profiles of three-dimensional (3D) winds by sequentially pointing the stabilized platform in different directions. A specific challenge is that the maximum angle off zenith is 8∘, which implies that the projection of the horizontal wind components onto the radar beam directions is a small component of Doppler velocity in most cases. A variational 3D wind retrieval technique is then described, allowing for 1 min 3D wind profiles to be retrieved. Statistical comparisons with 3-hourly radiosonde launches from the ship indicate that horizontal wind profiles can be obtained from such cloud radar observations at small off-zenith angles with biases less than 0.2 m s−1 and standard deviations of differences with radiosonde winds less than 2.5 m s−1.