Calibration and validation processing for the CYGNSS wind speed retrieval algorithm

Abstract. The new mission concept of microwave and infrared-laser occultation between low-Earth-orbit satellites (LMIO) is designed to provide accurate and long-term stable profiles of atmospheric thermodynamic variables, greenhouse gases (GHGs), and line-of-sight (l.o.s.) wind speed with focus on the upper troposphere and lower stratosphere (UTLS). While the unique quality of GHG retrievals enabled by LMIO over the UTLS has been recently demonstrated based on end-to-end simulations, the promise of l.o.s. wind retrieval, and of joint GHG and wind retrieval, has not yet been analyzed in any realistic simulation setting. Here we use a newly developed l.o.s. wind retrieval algorithm, which we embedded in an end-to-end simulation framework that also includes the retrieval of thermodynamic variables and GHGs, and analyze the performance of both stand-alone wind retrieval and joint wind and GHG retrieval. The wind algorithm utilizes LMIO laser signals placed on the inflection points at the wings of the highly symmetric C18OO absorption line near 4767 cm−1 and exploits transmission differences from a wind-induced Doppler shift. Based on realistic example cases for a diversity of atmospheric conditions, ranging from tropical to high-latitude winter, we find that the retrieved l.o.s. wind profiles are of high quality over the lower stratosphere under all conditions, i.e., unbiased and accurate to within about 2 m s−1 over about 15 to 35 km. The wind accuracy degrades into the upper troposphere due to the decreasing signal-to-noise ratio of the wind-induced differential transmission signals. The GHG retrieval in windy air is not vulnerable to wind speed uncertainties up to about 10 m s−1 but is found to benefit in the case of higher speeds from the integrated wind retrieval that enables correction of wind-induced Doppler shift of GHG signals. Overall both the l.o.s. wind and GHG retrieval results are strongly encouraging towards further development and implementation of a LMIO mission.

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Ocean Wind Speed Retrieval Algorithm using the frequency 36GHz Vertical/Horizontal and 6GHz Horizontal Data of the Advanced Microwave Scanning Radiometer (AMSR)

European Journal of Remote Sensing ◽

10.5721/eujrs20124513 ◽

2012 ◽

Vol 45 (1) ◽

pp. 133-140 ◽

Cited By ~ 1

Author(s):

Akira Shibata

Keyword(s):

Wind Speed ◽

Retrieval Algorithm ◽

Ocean Wind

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A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0128.1 ◽

2016 ◽

Vol 33 (7) ◽

pp. 1363-1375 ◽

Cited By ~ 5

Author(s):

Sungwook Hong ◽

Hwa-Jeong Seo ◽

Young-Joo Kwon

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Japan Meteorological Agency ◽

Retrieval Algorithm ◽

Sea Surface Wind Speed ◽

Brightness Temperatures ◽

Sea Surface Wind ◽

Medium Range

AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

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NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products

10.5194/egusphere-egu2020-18916 ◽

2020 ◽

Author(s):

Matthew Hammond ◽

Giuseppe Foti ◽

Christine Gommenginger ◽

Meric Srokosz ◽

Martin Unwin ◽

...

Keyword(s):

Wind Speed ◽

Sea Ice ◽

Global Ocean ◽

Retrieval Algorithm ◽

Navigation Satellite ◽

Validation Data ◽

Global Navigation ◽

Ice Detection ◽

Global Navigation Satellite ◽

Ocean Wind

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth&#8217;s surface. Using GNSS-R data collected by the UK TechDemoSat-1 (TDS-1) between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R wind speed retrieval algorithm called the Calibrated Bistatic Radar Equation (C-BRE), which now features updated data quality control mechanisms including flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform. Here we present an assessment of the performance of the latest NOC GNSS-R ocean wind speed and sea-ice products (NOC C-BRE v1.0) using validation data from the ECMWF ERA-5 re-analysis model output. Results show the capability of spaceborne GNSS-R sensors for accurate wind speed retrieval and sea-ice detection. Additionally, ground-processed Galileo returns collected by TDS-1 are examined and the geophysical sensitivity of reflected Galileo data to surface parameters is investigated. Preliminary results demonstrate the feasibility of spaceborne GNSS-R instruments receiving a combination of GNSS signals transmitted by multiple navigation systems, which offers the opportunity for frequent, high-quality ocean wind and sea-ice retrievals at low relative cost. Other advancements in GNSS-R technology are represented by future mission concepts such as HydroGNSS, a proposed ESA Scout mission opportunity by SSTL offering support for enhanced retrieval capabilities exploiting dual polarisation, dual frequency, and coherent reflected signal reception.

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Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

Remote Sensing ◽

10.3390/rs13224565 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4565

Author(s):

Maria Panfilova ◽

Vladimir Karaev

Keyword(s):

Wind Speed ◽

Good Accuracy ◽

Retrieval Algorithm ◽

Dual Frequency ◽

Precipitation Radar ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Ku Band ◽

Wide Swath

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

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Retrieved wind speed from the Orbiting Carbon Observatory-2

Atmospheric Measurement Techniques ◽

10.5194/amt-13-6889-2020 ◽

2020 ◽

Vol 13 (12) ◽

pp. 6889-6899

Author(s):

Robert R. Nelson ◽

Annmarie Eldering ◽

David Crisp ◽

Aronne J. Merrelli ◽

Christopher W. O'Dell

Keyword(s):

Wind Speed ◽

Near Infrared ◽

Surface Wind ◽

Surface Wind Speed ◽

Retrieval Algorithm ◽

Economic Sectors ◽

Wind Speeds ◽

Clear Sky ◽

Potential Benefits ◽

Microwave Sensors

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.

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Wind Speed Retrieval Based on Sea Surface Roughness Measurements from Spaceborne Microwave Radiometers

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0209.1 ◽

2013 ◽

Vol 52 (2) ◽

pp. 507-516 ◽

Cited By ~ 10

Author(s):

Sungwook Hong ◽

Inchul Shin

Keyword(s):

Surface Roughness ◽

Wind Speed ◽

Data Assimilation ◽

Thermal Emission ◽

Surface Wind ◽

Sea Surface ◽

Retrieval Algorithm ◽

Surface Emissivity ◽

Wide Range ◽

Global Data

AbstractWind speed is the main factor responsible for the increase in ocean thermal emission because sea surface emissivity strongly depends on surface roughness. An alternative approach to estimate the surface wind speed (SWS) as a function of surface roughness is developed in this study. For the sea surface emissivity, the state-of-the-art forward Fast Microwave Emissivity Model, version 3 (FASTEM-3), which is applicable for a wide range of microwave frequencies at incidence angles of less than 60°, is used. Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer (AMSR-E) observations are simulated using FASTEM-3 and the Global Data Assimilation and Prediction System operated by the Korea Meteorological Administration. The performance of the SWS retrieval algorithm is assessed by comparing its SWS output to that of the Global Data Assimilation System operated by the National Centers for Environmental Prediction. The surface roughness is computed using the Hong approximation and characteristics of the polarization ratio. When compared with the Tropical Atmosphere–Ocean data, the bias and root-mean-square error (RMSE) of the SWS outputs from the proposed wind speed retrieval algorithm were found to be 0.32 m s−1 (bias) and 0.37 m s−1 (RMSE) for the AMSR-E 18.7-GHz channel, 0.38 m s−1 (bias) and 0.42 m s−1 (RMSE) for the AMSR-E 23.8-GHz channel, and 0.45 m s−1 (bias) and 0.49 m s−1 (RMSE) for the AMSR-E 36.5-GHz channel. Consequently, this research provides an alternative method to retrieve the SWS with minimal a priori information on the sea surface.

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Development for wind friction velocity retrieval algorithm based on the SFMR and NOAA dropwindsondes measurements in hurricane conditions

10.5194/egusphere-egu21-9086 ◽

2021 ◽

Author(s):

Evgeny Poplavsky ◽

Nikita Rusakov ◽

Olga Ermakova ◽

Daniil Sergeev ◽

Yuliya Troitskaya ◽

...

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Atmospheric Boundary Layer ◽

Friction Velocity ◽

Microwave Radiometer ◽

Field Measurements ◽

Retrieval Algorithm ◽

Self Similarity ◽

Defect Profile ◽

Stepped Frequency

The work is concerned with the development of a method for the retrieval of tropical cyclones boundary atmospheric layer parameters, namely the wind friction velocity and wind speed at meteorological height. For the analysis, we used the results of field measurements of wind speed profiles from dropwindsondes launched from National Oceanic and Atmospheric Administration (NOAA) aircraft and collocated data from the Stepped-Frequency Microwave Radiometer (SFMR) located onboard of the same aircraft.The results of radiometric measurements were used to obtain the emissivity values, which were compared with the field data obtained from the falling dropwindsondes. Using the algorithm taking into account the self-similarity of the velocity defect profile (Ermakova et al., 2019), the parameters of the atmospheric boundary layer were determined from the data measured by dropwindsondes. This algorithm gives an opportunity to obtain the wind speed value at meteorological height and wind friction velocity from the averaged data in the wake part of the profiles of the marine atmospheric boundary layer.A comparison of the wind speed U10 dependencies, retrieved from the SFMR data and measurements from dropwindsondes, with the similar dependencies obtained in (Uhlhorn et al., 2007), was made, and their satisfactory agreement was demonstrated. This work was supported by the RFBR projects No. 19-05-00249, 19-05-00366.

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Validating precision estimates in horizontal wind measurements from a Doppler lidar

Atmospheric Measurement Techniques ◽

10.5194/amt-10-1229-2017 ◽

2017 ◽

Vol 10 (3) ◽

pp. 1229-1240 ◽

Cited By ~ 22

Author(s):

Rob K. Newsom ◽

W. Alan Brewer ◽

James M. Wilczak ◽

Daniel E. Wolfe ◽

Steven P. Oncley ◽

...

Keyword(s):

Wind Speed ◽

Radial Velocity ◽

Sonic Anemometer ◽

Direct Calculation ◽

Measurement Precision ◽

Horizontal Wind ◽

Retrieval Algorithm ◽

Doppler Lidar ◽

Wind Fields ◽

Wind Retrieval

Abstract. Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

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