Wind Speed Retrieval Research Articles

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed (SSWS) from HH-polarized Sentinel-1 (S1) SAR images. The Polarization Ratio (PR) models combined with the CMOD5.N Geophysical Model Function (GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HH-polarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation (BP) neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%, respectively, while compared to the ASCAT dataset the three parameters of training set are −0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

Studies of a Rapid Change Detector Using CYGNSS Level-2 Wind Speed Products

This work further investigates the use of ocean wind speed retrievals from NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission that are closely spaced in space and in time to detect regions in the atmosphere undergoing “rapid” change. As in a previous investigation, CYGNSS measurement “clumps” (i.e., groups of wind speed measurements satisfying specified time/space separation criteria) are used to create a wind speed change detector, and the results are compared to a set of atmospheric properties derived from Modern-Era Retrospective Analysis for Research and Applications 2 model datasets. The analysis uses a four-year CYGNSS Level-2 wind speed dataset developed by the National Oceanic and Atmospheric Administration and highlights the potential for using CYGNSS's wind speed measurements to locate various dynamic activities occurring over the ocean's surface. The presented results also highlight some of the challenges of the method, including the inherent dependence of detection performance on the quality of CYGNSS's wind speed estimates as well as the validity of comparisons to “truth” datasets having temporal resolutions much coarser than those provided by CYGNSS.

Evaluation of Sea Surface Winds and Waves Retrieved From the Chinese HY-2B Data

The wind and wave derived from the scatterometer and altimeter on board the Chinese Haiyang-2B (HY-2B) satellite were systemically evaluated in this article. The analysis of matchups between advanced scatterometer and the winds from HY-2B scatterometer with wind speeds up to 25 m/s showed a 0.78 m/s root-mean-square error (RMSE) with a 0.97 correlation (COR), superior to the 1.2 m/s RMSE and 0.93 COR of winds from the altimeter of HY-2B. Simultaneously, significant wave heights (SWHs) measured from HY-2B were collocated with measurements from the Jason-3 altimeter. Analysis of the calibration concluded that the observations from the HY-2B altimeter performed well at low-to-moderate sea states, yielding an RMSE of 0.29 m for SWH with a 0.98 COR for geophysical data records (GDRs) data after systematic corrections using the MOE determination method. In particular there were 20 typhoons occurring in China Seas during 2019–2020. The evaluation of high SWH when compared with those (SWH > 7 m) from Jason-3/WW3 indicated that HY-2B is accurate, with a 0.87 m/0.68 m RMSE and a 0.85 COR with SWH. Finally, statistical analysis of wind speed and SWH was investigated through waves simulated from the WAVEWATCH-III (WW3)/hybrid coordinate ocean using the current and the sea surface temperature (SST). The HY-2B wind measurements performed well at sea surface currents below 1 m/s and SWH less than 3 m, while a term for SST should be included in the scatterometer wind speed retrieval. In addition, wave states likely affect the accuracy of HY-2B wave speed measurements.

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

ABSTRACT This work investigates the effect of the resampling spatial resolution (RSR) on the retrieval of ocean surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images. For this purpose, 358 RADARSAT-2(RS-2) fine-beam quad-polarized SAR images and in-situ National Data Buoy Centre (NDBC) and China State Oceanic Administration (SOA) buoy measurements from selected geographic locations are used in this paper. In order to obtain better accuracy SAR-retrieved wind speeds, CMOD5.N function, Z2011 polarization ratio (PR) model and C2PO model constitute the best models for VV-, HH- and cross-polarized SAR images, respectively. The resulting statistical analysis suggests that better accuracy wind speeds can be retrieved at somewhat coarser resolutions. The optimal RSR for the VV-, HH-, VH- and HV-polarized channels are 850, 1050,1350 and 1450 m, respectively, with RMSEs of 1.74, 2.03, 1.61 and 1.83 m/s.

Retrieved wind speed from the Orbiting Carbon Observatory-2

Abstract. Satellite measurements of surface wind speed over the ocean inform a wide variety of scientific pursuits. While both active and passive microwave sensors are traditionally used to detect surface wind speed over water surfaces, measurements of reflected sunlight in the near-infrared made by the Orbiting Carbon Observatory-2 (OCO-2) are also sensitive to the wind speed. In this work, retrieved wind speeds from OCO-2 glint measurements are validated against the Advanced Microwave Scanning Radiometer-2 (AMSR2). Both sensors are in the international Afternoon Constellation (A-Train), allowing for a large number of co-located observations. Several different OCO-2 retrieval algorithm modifications are tested, with the most successful being a single-band Cox–Munk-only model. Using this, we find excellent agreement between the two sensors, with OCO-2 having a small mean bias against AMSR2 of −0.22 m s−1, an RMSD of 0.75 m s−1, and a correlation coefficient of 0.94. Although OCO-2 is restricted to clear-sky measurements, potential benefits of its higher spatial resolution relative to microwave instruments include the study of coastal wind processes, which may be able to inform certain economic sectors.

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter (IALT) is a new type of radar altimeter system. In contrast to the conventional nadir-looking altimeters, such as HY-2A altimeter, Jason-1/2, and TOPEX/Poseidon, IALT observes the earth surface at low incident angles (2.5°–8°), so its swath is much wider and its spatial resolution is much higher than the previous altimeters. This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station. Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles, a neural network is used to retrieve the ocean surface wind speed in this study. The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed, buoy wind speed, and in-situ ship measurements. The results show that the retrieved wind speed bias is about −0.21 m/s, and the root-mean-square (RMS) error is about 1.85 m/s. The wind speed accuracy of IALT meets the performance requirement.

Wind Speed, Surface Flux, and Intraseasonal Convection Coupling From CYGNSS Data

AbstractThis study analyzes wind speed and surface latent heat flux anomalies from the Cyclone Global Navigation Satellite System (CYGNSS), aiming to understand the physical mechanisms regulating intraseasonal convection, particularly associated with the Madden‐Julian oscillation (MJO). An advantage of CYGNSS compared to other space‐based data sets is that its surface wind speed retrievals have reduced attenuation by precipitation, thus providing improved information about the importance of wind‐induced surface fluxes for the maintenance of convection. Consistent with previous studies from buoys, CYGNSS shows that enhanced MJO precipitation is associated with enhanced wind speeds, and that associated surface heat flux anomalies have a magnitude about 7–12% of precipitation anomalies. Thus, latent heat flux anomalies are an important maintenance mechanism for MJO convection through the column moist static energy budget. A composite analysis during boreal summer over the eastern north Pacific also supports the idea that wind‐induced surface flux is important for MJO maintenance there.

Observation of vertical wind profiling with lidar based on correction of sensitivity.

A high spectral resolution lidar (HSRL) for simultaneously detecting vertical wind, temperature, and the backscattering ratio in the troposphere is developed. The atmospheric temperature and vertical wind are determined by the Rayleigh scattering spectrum width and Mie scattering spectrum Doppler shift, respectively. The influence of temperature and the backscattering ratio on vertical wind measurement accuracy is also analyzed. The temperature and backscattering ratio affect the wind measurement, which produces the vertical wind offset. A correction considering the effects of the method is conducted considering real-time and on-site temperature profiles and the backscattering ratio to correct wind measurement sensitivity. Measurements of HSRL taken under different weather conditions (fine and hazy days) are demonstrated. Good agreement between the HSRL and the radiosonde measurements was obtained considering lapse rates and temperature inversions. The maximum temperature offsets were 1.3 and 4 K at a height of 1.5 km on fine and hazy days, respectively. Then, real-time and on-site temperature profiles and backscattering ratios were applied to correct the real-time and on-site wind. The corrected wind profiles showed satisfactory agreement with the wind profiles acquired from the calibrated wind lidar. The maximum detection offsets of the retrieved wind speed were reduced from 1 m/s to 0.55 m/s and from 1 m/s to 0.21 m/s, respectively, which were decreases of 0.45 and 0.79 m/s in fine and hazy days after correction of sensitivity. It is evident that the corrected wind method can reduce the influence of temperature and the backscattering ratio on the wind measurement and the offset of vertical wind. The reliability of the method is also proven.

Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic.

Cyclone Wind Retrieval Based on X-Band SAR-Derived Wave Parameter Estimation

AbstractIn this study, a method for retrieving wind speed from synthetic aperture radar (SAR) imagery collected under extreme weather conditions is proposed. The rationale for this approach relies on the fact that, although copolarized channels exhibit saturation for wind speed >~20 m s−1, the wave growth can be successfully exploited to gather information on wind speed under extreme weather conditions. Hence, in this study, the intrinsic relationship among the wind-wave triplets [wind speed at 10 m above the sea surface, significant wave height (SWH), and peak wave period] is exploited in order to retrieve wind speeds under tropical cyclone conditions. Experiments, undertaken on actual X-bandTerraSAR-X(TS-X) SAR images of tropical cyclones (Typhoon Megi, Hurricane Sandy, and Hurricane Miriam) and using collocated WAVEWATCH-III (WW3) simulations, revealed the robustness of the proposed approach, which resulted in a root-mean-square error (RMSE) of 2.54 m s−1when comparing the retrieved wind speeds with the values from products delivered by the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD). However, the applicability of the algorithm herein will be further confirmed at very strong storms.

Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning

HY2B is now the latest altimetry mission that provides global nadir significant wave height (SWH) and sea surface wind speed. The validation and calibration of HY2B are carried out against National Data Buoy Center (NDBC) buoy observations from April 2019 to April 2020. In general, the HY2B altimeter measurements agree well with buoy observation, with scatter index of 9.4% for SWH, and 15.1% for wind speed. However, we observed a significant bias of 0.14 m for SWH and −0.42 m/s for wind speed. A deep learning technique is novelly applied for the calibration of HY2B SWH and wind speed. Deep neural network (DNN) is built and trained to correct SWH and wind speed by using input from parameters provided by the altimeter such as sigma0, sigma0 standard deviation (STD). The results based on DNN show a significant reduction of the bias, root mean square error (RMSE), and scatter index (SI) for both SWH and wind speed. Several DNN schemes based on different combination of input parameters have been examined in order to obtain the best model for the calibration. The analysis reveals that sigma0 STD is a key parameter for the calibration of HY2B SWH and wind speed.

Neural Network Based Quality Control of CYGNSS Wind Retrieval

Global Navigation Satellite System – Reflectometry (GNSS-R) is a relatively new field in remote sensing that uses reflected GPS signals from the Earth’s surface to study the state of the surface geophysical parameters under observation. The CYGNSS is a first of its kind GNSS-R constellation mission launched in December 2016. It aims at providing high quality global scale GNSS-R measurements that can reliably be used for ocean science applications such as the study of ocean wind speed dynamics, tropical cyclone genesis, coupled ocean wave modelling, and assimilation into Numerical Weather Prediction models. To achieve this goal, strong quality control filters are needed to detect and remove outlier measurements. Currently, quality control of CYGNSS data products are based on fixed thresholds on various engineering, instrument, and measurement conditions. In this work we develop a Neural Network based quality control filter for automated outlier detection of CYGNSS retrieved winds. The primary merit of the proposed ML filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate thresholded flags for each one. Use of Machine Learning capabilities to capture inherent patterns in the data can create an efficient and effective mechanism to detect and remove outlier measurements. The resulting filter has a probability of outlier detection (PD) >75% and False Alarm Rate (FAR) < 20% for a wind speed range of 5 to 18 m/s. At least 75% of the outliers with wind speed errors of at least 5 m/s are removed while ~100% of the outliers with wind speed errors of at least 10 m/s are removed. This filter significantly improves data quality. The standard deviation of wind speed retrieval error is reduced from 2.6 m/s without the filter to 1.7 m/s with it over a wind speed range of 0 to 25 m/s. The design space for this filter is also analyzed in this work to characterize trade-offs between PD and FAR. Currently the filter performance is applicable only up to moderate wind speeds, as sufficient data is available only in this range to train the filter, as a way forward, more data over time can help expand the usability of this filter to higher wind speed ranges as well.

Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

AbstractRecent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s−1. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s−1, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

Uncertainties in Ocean Latent Heat Flux Variations over Recent Decades in Satellite-Based Estimates and Reduced Observation Reanalyses

AbstractFour state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.

Retrieval of Ocean Surface Wind Speed Using Reflected BPSK/BOC Signals

The Global Navigation Satellite System (GNSS) has become a valuable resource as a remote sensing technique. In the past decade, the use of reflected GNSS signals for sensing the Earth, also known as GNSS reflectometry (GNSS-R), has grown rapidly. On the other hand, with the continuous development of GNSS, multi-frequency multi-modulation signals have been used to enhance not only positioning performance, but also remote sensing applications. It is known that for some constellations, navigation satellites broadcast signals employing BPSK (binary phase-shift keying) modulation and BOC (binary offset carrier) modulation at the same frequency band. This paper proposes a new GNSS-R measurement, called a composite delay-Doppler map (cDDM), by utilizing the received reflected GNSS signals with different modulation techniques for the purpose of retrieving wind speed. The GNSS-R receiver can receive BPSK and BOC signals simultaneously at the same frequency band (e.g., GPS III L1 C/A and L1C or QZSS L1 C/A and L1C) and process the signals to generate GNSS-R measurements. Exploration of the observable features extracted from the composite DDM and the wind speed retrieval algorithm are also provided. The simulation verifies the proposed method under a configuration that is specified for the orbital and instrument specification of the upcoming TRITON mission.

Fast dual‐doppler LiDAR retrieval of boundary layer wind profiles

Operational doppler LiDARs (light detection and ranging) are often heavily engaged in specialised phenomenon detection, as well as dedicated surveillance scans, leaving little room for further additions to the tightly packed scanning strategy. This paper presents a fast method for retrieval of the urban boundary layer wind profile using existing scan data from two long-range doppler LiDARs at the Hong Kong International Airport (HKIA). Leveraging occasional range height indicator scans originally designed to aid the monitoring of terrain-induced wind disturbances, vertical profiles of the horizontal winds between 150 and 750m above ground level are estimated near the intersection point. The LiDAR-derived wind profiles are validated against measurements from a nearby wind profiler, as well as Aircraft Meteorological Data Relay (AMDAR) flight observations, for a 1-year period, 2019. Results indicated close agreement in terms of wind speed and direction between the LiDAR-derived profiles and wind profiler measurements, particularly above 300m. A slant angle correction factor is found to effectively alleviate the underestimation of the horizontal wind components at higher beam angles while also improving the consistency of the retrieved wind speed profile across different altitudes. For the AMDAR comparison, large discrepancies in wind speed and direction might be attributed to the natural variability of glide path winds well-known to HKIA. This study demonstrates the potential of extracting additional boundary layer wind information from ‘part-time’ scans of operational doppler LiDARs.

A Forward Model for Data Assimilation of GNSS Ocean Reflectometry Delay-Doppler Maps

Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples.

A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements

The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain.

A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

Wind retrieval from temperature measurements from the Rover Environmental Monitoring Station/Mars Science Laboratory

This work presents a novel method for the real-time retrieval of wind speed on the surface of Mars that uses temperature measurements from the Rover Environmental Monitoring Station (REMS) instrument onboard the Curiosity rover of the Mars Science Laboratory (MSL) mission. After final failure of the Wind Sensor (WS) in sol 1491, REMS has not been providing wind data. The new wind retrieval approach that we propose may eventually be able to supply MSL with wind values for contextualizing the rover’s operations and for meteorological studies on the surface of Mars. The new method is based on forced convection modeling of the Air Temperature Sensors (ATS) of REMS as thin rods immersed in the extreme low-pressure and high-radiating atmospheric conditions of the Martian thermal boundary layer at a height of ∼1.5 m from the surface. A preliminary validation of the possibilities and limitations of this retrieval has been performed using comparative analysis with existing REMS wind field-site data for the same sols that are available at the Planetary Data System (PDS). We have developed both a “coarse” approach, in which wind speed is determined with no regard to wind direction, and a “refined” method, in which it is attempted to determine both wind speed and direction. Assuming the previously reported WS retrieval errors of 20% for the wind speed, we report an agreement to the WS values of wind speed ranging from 36.4% to 77% of the acquisition time for the “coarse” approach, depending on the sol examined. These promising results are limited to only evening extended acquisitions from 18:00 to 21:00 local mean solar time (LMST). This method could be applied to daytime conditions. The results suggest a new optimal orientation for wind speed retrieval of +60°clockwise with respect to the forward direction of the Curiosity rover, although the technique is not yet ready to be considered for planning of the Curiosity rover operations. This method could extend the wind characterization of the Gale Crater for future Curiosity rover data acquisitions by recycling air temperature measurements and provide the scientific community with a data set for future comparative analysis with the Temperature and Wind Sensors for InSight (TWINS)/InSight, the HabitAbility: Brines, Irradiation and Temperature (HABIT)/ExoMars 2022, and the Mars Environmental Dynamics Analyzer (MEDA)/Mars 2020 rover instruments.