scholarly journals Determining Tropical Cyclone Surface Wind Speed Structure and Intensity with the CYGNSS Satellite Constellation

2017 ◽  
Vol 56 (7) ◽  
pp. 1847-1865 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Measurement Errors ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Maximum Surface ◽  
Wind Speed Distribution ◽  
Global Navigation Satellite ◽  
Future Work

AbstractThe Cyclone Global Navigation Satellite System (CYGNSS) consists of a constellation of eight microsatellites that provide observations of surface wind speed in all precipitating conditions. A method for estimating tropical cyclone (TC) metrics—maximum surface wind speed VMAX, radius of maximum surface wind speed RMAX, and wind radii (R64, R50, and R34)—from CYGNSS observations is developed and tested using simulated CYGNSS observations with realistic measurement errors. Using two inputs, 1) CYGNSS observations and 2) the storm center location, estimates of TC metrics are possible through the use of a parametric wind model algorithm that effectively interpolates between the available observations as a constraint on the assumed wind speed distribution. This methodology has a promising performance as evaluated from the simulations presented. In particular, after quality-control filters based on sampling properties are applied to the population of test cases, the standard deviation of retrieval error for VMAX is 4.3 m s−1 (where 1 m s−1 = 1.94 kt), for RMAX is 17.4 km, for R64 is 16.8 km, for R50 is 21.6 km, and for R34 is 41.3 km (where 1 km = 0.54 n mi). These TC data products will be available for the 2017 Atlantic Ocean hurricane season using on-orbit CYGNSS observations, but near-real-time operations are the subject of future work. Future work will also include calibration and validation of the algorithm once real CYGNSS data are available.

scholarly journals CYGNSS Ocean Surface Wind Validation in the Tropics

2020 ◽  
Author(s):  
Shakeel Asharaf ◽  
Duane E. Waliser ◽  
Derek J. Posselt ◽  
Christopher S. Ruf ◽  
Chidong Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Cold Pool ◽  
Tropical Ocean ◽  
Near Surface ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
Global Navigation Satellite

AbstractSurface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass and momentum across the Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near surface wind speed over the tropical and sub-tropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s-1 root mean squared difference, meeting the NASA science mission Level-1 uncertainty requirement for wind speeds below 20 m s-1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air-sea interaction in regions that are relatively under-sampled by other observing platforms.

scholarly journals Estimating Tropical Cyclone Integrated Kinetic Energy with the CYGNSS Satellite Constellation

2017 ◽  
Vol 56 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Tropical Cyclones ◽  
Measurement Errors ◽  
Inner Core ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Insurance Companies ◽  
High Temporal Resolution

AbstractThe Cyclone Global Navigation Satellite System (CYGNSS) constellation is designed to provide observations of surface wind speed in and near the inner core of tropical cyclones with high temporal resolution throughout the storm’s life cycle. A method is developed for estimating tropical cyclone integrated kinetic energy (IKE) using CYGNSS observations. IKE is calculated for each geographically based quadrant out to an estimate of the 34-kt (1 kt = 0.51 m s−1) wind radius. The CYGNSS-IKE estimator is tested and its performance is characterized using simulated CYGNSS observations with realistic measurement errors. CYGNSS-IKE performance improves for stronger, more organized storms and with increasing number of observations over the extent of the 34-kt radius. Known sampling information can be used for quality control. While CYGNSS-IKE is calculated for individual geographic quadrants, using a total-IKE—a sum over all quadrants—improves performance. CYGNSS-IKE should be of interest to operational and research meteorologists, insurance companies, and others interested in the destructive potential of tropical cyclones developing in data-sparse regions, which will now be covered by CYGNSS. The CYGNSS-IKE product will be available for the 2017 Atlantic Ocean hurricane season.

scholarly journals Validation of NOAA CyGNSS Wind Speed Product with the CCMP Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1832
Author(s):  
Xiaohui Li ◽  
Dongkai Yang ◽  
Jingsong Yang ◽  
Guoqi Han ◽  
Gang Zheng ◽  
...  
Keyword(s):  
Wind Speed ◽  
Comprehensive Evaluation ◽  
Surface Wind ◽  
Temporal Analysis ◽  
Satellite System ◽  
Wind Retrieval ◽  
Sea Surface Wind ◽  
Geophysical Model ◽  
Geophysical Model Function ◽  
Global Navigation Satellite

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds.

scholarly journals Tropical Cyclone Center Fix Using CYGNSS Winds

2019 ◽  
Vol 58 (9) ◽  
pp. 1993-2003 ◽  
Author(s):  
David Mayers ◽  
Christopher Ruf
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Satellite System ◽  
Center Location ◽  
Residual Difference ◽  
Measurement Results ◽  
Storm Center ◽  
Global Navigation Satellite ◽  
Cross Track ◽  
Along Track

AbstractA new method is described for determining the center location of a tropical cyclone (TC) using wind speed measurements by the NASA Cyclone Global Navigation Satellite System (CYGNSS). CYGNSS measurements made during TC overpasses are used to constrain a parametric wind speed model in which storm center location is varied. The “MTrack” storm center location is selected to minimize the residual difference between model and measurement. Results of the MTrack center fix are compared to the National Hurricane Center (NHC) Best Track, the Automated Rotational Center Hurricane Eye Retrieval (ARCHER), and aircraft reconnaissance fixes for category 1–category 3 TCs during the 2017 and 2018 hurricane seasons. MTrack produces storm center locations at intermediate times between NHC fixes with a factor of 5.6 overall reduction in sensitivity to uncertainties in the NHC fixes between which it interpolates. The MTrack uncertainty is found to be larger in the cross-track direction than the along-track direction, although this behavior and the absolute accuracy of position estimates require further investigation.

scholarly journals Tropical Cyclone Destructive Potential by Integrated Kinetic Energy

2007 ◽  
Vol 88 (4) ◽  
pp. 513-526 ◽  
Author(s):  
Mark D. Powell ◽  
Timothy A. Reinhold
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Wind Loading ◽  
Wind Fields ◽  
Damage Potential ◽  
Spatial Coverage ◽  
Destructive Potential

Tropical cyclone damage potential, as currently defined by the Saffir-Simpson scale and the maximum sustained surface wind speed in the storm, fails to consider the area impact of winds likely to force surge and waves or cause particular levels of damage. Integrated kinetic energy represents a framework that captures the physical process of ocean surface stress forcing waves and surge while also taking into account structural wind loading and the spatial coverage of the wind. Integrated kinetic energy was computed from gridded, objectively analyzed surface wind fields of 23 hurricanes representing large and small storms. A wind destructive potential rating was constructed by weighting wind speed threshold contributions to the integrated kinetic energy, based on observed damage in Hurricanes Andrew, Hugo, and Opal. A combined storm surge and wave destructive potential rating was assigned according to the integrated kinetic energy contributed by winds greater than tropical storm force. The ratings are based on the familiar 1–5 range, with continuous fits to allow for storms as weak as 0.1 or as strong as 5.99.

scholarly journals Potentials in Improving Predictability of Multiscale Tropical Weather Systems Evaluated through Ensemble Assimilation of Simulated Satellite-Based Observations

2018 ◽  
Vol 75 (5) ◽  
pp. 1675-1698 ◽  
Author(s):  
Yue Ying ◽  
Fuqing Zhang
Keyword(s):  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Lead Times ◽  
Atmospheric Motion ◽  
Tropical Weather ◽  
Global Navigation Satellite ◽  
Atmospheric Motion Vectors ◽  
The Impact

As a follow-up of our recent paper on the practical and intrinsic predictability of multiscale tropical weather and equatorial waves, this study explores the potentials in improving the analysis and prediction of these weather systems through assimilating simulated satellite-based observations with a regional ensemble Kalman filter (EnKF). The observing networks investigated include the retrieved temperature and humidity profiles from the Advanced TIROS Operational Vertical Sounder (ATOVS) and global positioning system radio occultation (GPSRO), the atmospheric motion vectors (AMVs), infrared brightness temperature from Meteosat-7 ( Met7-Tb), and retrieved surface wind speed from the Cyclone Global Navigation Satellite System (CYGNSS). It is found that assimilating simulated ATOVS thermodynamic profiles and AMV winds improves the accuracy of wind, temperature, humidity, and hydrometeors for scales larger than 200 km. The skillful forecast lead times can be extended by as much as 4 days for scales larger than 1000 km. Assimilation of Met7-Tb further improves the analysis of cloud hydrometeors even at scales smaller than 200 km. Assimilating CYGNSS surface winds further improves the low-level wind and temperature. Meanwhile, the impact from assimilating the current-generation GPSRO data with better vertical resolution and accuracy is comparable to assimilating half of the current ATOVS profiles, while a hypothetical 25-times increase in the number of GPSRO profiles can potentially exceed the impact from assimilating the current network of retrieved ATOVS profiles. Our study not only shows great promises in further improving practical predictability of multiscale equatorial systems but also provides guidance in the evaluation and design of current and future spaceborne observations for tropical weather.

scholarly journals Improvements to the Operational Tropical Cyclone Wind Speed Probability Model

2013 ◽  
Vol 28 (3) ◽  
pp. 586-602 ◽  
Author(s):  
Mark DeMaria ◽  
John A. Knaff ◽  
Michael J. Brennan ◽  
Daniel Brown ◽  
Richard D. Knabb ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
North Pacific ◽  
Probability Model ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Multiple Time ◽  
Probability Estimates ◽  
Error Distributions ◽  
Most Significant Change

Abstract The National Hurricane Center Hurricane Probability Program, which estimated the probability of a tropical cyclone passing within a specific distance of a selected set of coastal stations, was replaced by the more general Tropical Cyclone Surface Wind Speed Probabilities in 2006. A Monte Carlo (MC) method is used to estimate the probabilities of 34-, 50-, and 64-kt (1 kt = 0.51 m s−1) winds at multiple time periods through 120 h. Versions of the MC model are available for the Atlantic, the combined eastern and central North Pacific, and the western North Pacific. This paper presents a verification of the operational runs of the MC model for the period 2008–11 and describes model improvements since 2007. The most significant change occurred in 2010 with the inclusion of a method to take into account the uncertainty of the track forecasts on a case-by-case basis, which is estimated from the spread of a dynamical model ensemble and other parameters. The previous version represented the track uncertainty from the error distributions from the previous 5 yr of forecasts from the operational centers, with no case-to-case variability. Results show the MC model provides robust estimates of the wind speed probabilities using a number of standard verification metrics, and that the inclusion of the case-by-case measure of track uncertainty improved the probability estimates. Beginning in 2008, an older operational wind speed probability table product was modified to include information from the MC model. This development and a verification of the new version of the table are described.

In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

2019 ◽  
Vol 100 (10) ◽  
pp. 2009-2023 ◽  
Author(s):  
Christopher Ruf ◽  
Shakeel Asharaf ◽  
Rajeswari Balasubramaniam ◽  
Scott Gleason ◽  
Timothy Lang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Dynamic Range ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Current Status ◽  
Temporal Sampling

AbstractThe NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

scholarly journals Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets

2019 ◽  
Vol 11 (23) ◽  
pp. 2747 ◽  
Author(s):  
Zhounan Dong ◽  
Shuanggen Jin
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Ocean Surface Wind ◽  
Global Navigation ◽  
Global Navigation Satellite

Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth’s surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean surface wind speed globally. In this paper, a refined spaceborne GNSS-R sea surface wind speed retrieval algorithm is presented and validated with the ground surface reference wind speed from numerical weather prediction (NWP) and cross-calibrated multi-platform ocean surface wind vector analysis product (CCMP), respectively. The results show that when the wind speed was less than 20 m/s, the RMS of the GNSS-R retrieved wind could achieve 1.84 m/s in the case where the NWP winds were used as the ground truth winds, while the result was better than the NWP-based retrieved wind speed with an RMS of 1.68 m/s when the CCMP winds were used. The two sets of inversion results were further evaluated by the buoy winds, and the uncertainties from the NWP-derived and CCMP-derived model prediction wind speed were 1.91 m/s and 1.87 m/s, respectively. The accuracy of inversed wind speeds for different GNSS pseudo-random noise (PRN) satellites and types was also analyzed and presented, which showed similar for different PRN satellites and different types of satellites.

scholarly journals Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results

2019 ◽  
Vol 11 (24) ◽  
pp. 3013 ◽  
Author(s):  
Cheng Jing ◽  
Xinliang Niu ◽  
Chongdi Duan ◽  
Feng Lu ◽  
Guodong Di ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Sea Surface ◽  
Satellite Mission ◽  
Preliminary Results ◽  
Sea Surface Wind Speed ◽  
Wind Speeds ◽  
Sea Surface Wind

Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval are presented. Empirical fully developed sea (FDS) geophysical model functions (GMFs) relating the normalized bistatic radar cross-section to the sea surface wind speed are proposed for the BF-1 GNSS-R instruments. The FDS GMFs are derived from the collocated BF-1 observations, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and the advanced scatterometer (ASCAT) satellite observations. The preliminary tests reveal that the root-mean-square error (RMSE) between the derived wind speed and the reanalysis is 2.63 m/s for wind speeds in the range of 0.5–40.5 m/s. Further comparisons with the ASCAT observations and mooring buoys show that the RMSEs are 2.04 m/s and 1.77 m/s, respectively, at low-to-moderate wind speeds. This study demonstrates the effectiveness of BF-1 and provides a basis for the future GMF development of the BF-1 A/B mission.

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