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 A Comparative Assessment of Surface Wind Speed and Sea Surface Temperature over the Indian Ocean by TMI, MSMR, and ERA-40

2007 ◽  
Vol 24 (6) ◽  
pp. 1131-1142 ◽  
Author(s):  
Anant Parekh ◽  
Rashmi Sharma ◽  
Abhijit Sarkar
Keyword(s):  
Wind Speed ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Wind Speeds ◽  
Low Wind Speeds

A 2-yr (June 1999–June 2001) observation of ocean surface wind speed (SWS) and sea surface temperature (SST) derived from microwave radiometer measurements made by a multifrequency scanning microwave radiometer (MSMR) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is compared with direct measurements by Indian Ocean buoys. Also, for the first time SWS and SST values of the same period obtained from 40-yr ECMWF Re-Analysis (ERA-40) have been evaluated with these buoy observations. The SWS and SST are shown to have standard deviations of 1.77 m s−1 and 0.60 K for TMI, 2.30 m s−1 and 2.0 K for MSMR, and 2.59 m s−1 and 0.68 K for ERA-40, respectively. Despite the fact that MSMR has a lower-frequency channel, larger values of bias and standard deviation (STD) are found compared to those of TMI. The performance of SST retrieval during the daytime is found to be better than that at nighttime. The analysis carried out for different seasons has raised an important question as to why one spaceborne instrument (TMI) yields retrievals with similar biases during both pre- and postmonsoon periods and the other (MSMR) yields drastically different results. The large bias at low wind speeds is believed to be due to the poorer sensitivity of microwave emissivity variations at low wind speeds. The extreme SWS case study (cyclonic condition) showed that satellite-retrieved SWS captured the trend and absolute magnitudes as reflected by in situ observations, while the model (ERA-40) failed to do so. This result has direct implications on the real-time application of satellite winds in monitoring extreme weather events.

scholarly journals Homogenization and Trend Analysis of Canadian Near-Surface Wind Speeds

2010 ◽  
Vol 23 (5) ◽  
pp. 1209-1225 ◽  
Author(s):  
Hui Wan ◽  
Xiaolan L. Wang ◽  
Val R. Swail
Keyword(s):  
Wind Speed ◽  
Trend Analysis ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
The United States ◽  
Homogeneity Test ◽  
Near Surface ◽  
Wind Speeds ◽  
Long Term Trends

Abstract Near-surface wind speeds recorded at 117 stations in Canada for the period from 1953 to 2006 were analyzed in this study. First, metadata and a logarithmic wind profile were used to adjust hourly wind speeds measured at nonstandard anemometer heights to the standard 10-m level. Monthly mean near-surface wind speed series were then derived and subjected to a statistical homogeneity test, with homogeneous monthly mean geostrophic wind (geowind) speed series being used as reference series. Homogenized monthly mean near-surface wind speed series were obtained by adjusting all significant mean shifts, using the results of the statistical test and modeling along with all available metadata, and were used to assess the long-term trends. This study shows that station relocation and anemometer height change are the main causes for discontinuities in the near-surface wind speed series, followed by instrumentation problems or changes, and observing environment changes. It also shows that the effects of artificial mean shifts on the results of trend analysis are remarkable, and that the homogenized near-surface wind speed series show good spatial consistency of trends, which are in agreement with long-term trends estimated from independent datasets, such as surface winds in the United States and cyclone activity indices and ocean wave heights in the region. These indicate success in the homogenization of the wind data. During the period analyzed, the homogenized near-surface wind speed series show significant decreases throughout western Canada and most parts of southern Canada (except the Maritimes) in all seasons, with significant increases in the central Canadian Arctic in all seasons and in the Maritimes in spring and autumn.

scholarly journals Centennial-scale variability of terrestrial near-surface wind speed over China from reanalysis

2021 ◽  
pp. 1-52
Author(s):  
Cheng Shen ◽  
Jinlin Zha ◽  
Jian Wu ◽  
Deming Zhao
Keyword(s):  
Wind Speed ◽  
Large Scale ◽  
Stepwise Regression ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Wind Speeds ◽  
Ocean Atmosphere ◽  
Forward Stepwise ◽  
Strong Winds

AbstractInvestigations of variations and causes of near-surface wind speed (NWS) further understanding of the atmospheric changes and improve the ability of climate analysis and projections. NWS varies on multiple temporal scales; however, the centennial-scale variability in NWS and associated causes over China remains unknown. In this study, we employ the European Centre for Medium-Range Weather Forecasts (ECMWF) twentieth century reanalysis (ERA-20C) to study the centennial-scale changes in NWS from 1900–2010. Meanwhile, a forward stepwise regression algorithm is used to reveal the relationships between NWS and large-scale ocean-atmosphere circulations. The results show three unique periods in annual mean NWS over China from 1900–2010. The annual mean NWS displayed a decreasing trend of -0.87% decade-1 and -11.75% decade-1 from 1900–1925 and 1957–2010, respectively, which were caused by the decreases in the days with strong winds, with trends of -6.64 and -4.66 days decade-1, respectively. The annual mean NWS showed an upward trend of 55.47% decade-1 from 1926–1956, which was caused by increases in the days with moderate (0.43 days decade-1) and strong winds (23.55 days decade-1). The reconstructed wind speeds based on forward stepwise regression algorithm matched well with the original wind speeds; therefore, the decadal changes in NWS over China at centennial-scale were mainly induced by large-scale ocean-atmosphere circulations, with the total explanation power of 66%. The strongest explanation power was found in winter (74%), and the weakest explanation power was found in summer (46%).

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.

scholarly journals Projected Near-Surface Wind Speed Trends in Lithuania

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5425
Author(s):  
Justė Jankevičienė ◽  
Arvydas Kanapickas
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Observational Data ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Green Energy ◽  
Near Surface ◽  
Wind Speeds ◽  
Global Circulation ◽  
Global Circulation Models

Developing wind energy in Lithuania is one of the most important ways to achieve green energy goals. Observational data show that the decline in wind speeds in the region may pose challenges for wind energy development. This study analyzed the long-term variation of the observed 2006–2020 and projected 2006–2100 near-surface wind speed at the height of 10 m over Lithuanian territory using data of three models included in the Coupled Model Intercomparison Project phase 5 (CMIP5). A slight decrease in wind speeds was found in the whole territory of Lithuania for the projected wind speed data of three global circulation models for the scenarios RCP2.6, RCP4.5, and RCP8.5. It was found that the most favorable scenario for wind energy production is RCP2.6, and the most unfavorable is the RCP4.5 scenario under which the decrease in wind speed may reach 12%. At the Baltic Sea coastal region, the decline was smaller than in the country’s inner regions by the end of the century. The highest reduction in speed is characteristic of the most severe RCP8.5 scenario. Although the analysis of wind speeds projected by global circulation models (GCM) confirms the downward trends in wind speeds found in the observational data, the projected changes in wind speeds are too small to significantly impact the development of wind farms in Lithuania.

scholarly journals Global Near-Surface Wind Speed Trends in Observation and CMIP6 Historical Simulation for 1850–2014

2020 ◽  
Author(s):  
Kaiqiang Deng ◽  
Cesar Azorin-Molina ◽  
Lorenzo Minola ◽  
Deliang Chen
Keyword(s):  
Wind Speed ◽  
Land Use Changes ◽  
Surface Wind ◽  
Coupled Model ◽  
Surface Wind Speed ◽  
Human Society ◽  
Pollution Dispersion ◽  
Near Surface ◽  
Wind Speeds ◽  
Ocean Wind

<p>The changes in near-surface (10-m height) wind speed have direct impacts on human society, such as utilization of wind energy, air pollution dispersion and dust storm frequency, which requires comprehensive assessment and improved understanding. Based on ground-based observations and multiple atmospheric reanalysis datasets, previous research revealed significant negative and positive trends in wind speed over land and oceans, respectively. In this study, we used Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations to investigate the association between global mean wind speed changes and human-induced forcing. It is found that both unforced pre-industrial control run and historical natural forcing experiments failed in reproducing the observed trends in land and ocean wind speeds. However, the CMIP6 historical greenhouse gas forcing successfully simulated the increasing trend in ocean wind speed, while the CMIP6 historical aerosol forcing and experiments with land use changes seemed to have caused a decreasing trend in wind speeds over both land and ocean, suggesting that anthropogenic forcings are crucial drivers for the recent changes in global wind speed. Further attribution studies are needed to better understand wind speed variability under a warming climate.</p>

Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer

2007 ◽  
Vol 135 (9) ◽  
pp. 3070-3085 ◽  
Author(s):  
Eric W. Uhlhorn ◽  
Peter G. Black ◽  
James L. Franklin ◽  
Mark Goodberlet ◽  
James Carswell ◽  
...  
Keyword(s):  
Wind Speed ◽  
Drag Coefficient ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Model Function ◽  
Near Surface ◽  
Wind Speeds ◽  
Extreme Wind ◽  
High Bias ◽  
Stepped Frequency

Abstract For the first time, the NOAA/Aircraft Operations Center (AOC) flew stepped frequency microwave radiometers (SFMRs) on both WP-3D research aircraft for operational hurricane surface wind speed measurement in 2005. An unprecedented number of major hurricanes provided ample data to evaluate both instrument performance and surface wind speed retrieval quality up to 70 m s−1 (Saffir–Simpson category 5). To this end, a new microwave emissivity–wind speed model function based on estimates of near-surface winds in hurricanes by global positioning system (GPS) dropwindsondes is proposed. For practical purposes, utilizing this function removes a previously documented high bias in moderate SFMR-measured wind speeds (10–50 m s−1), and additionally corrects an extreme wind speed (>60 m s−1) underestimate. The AOC operational SFMRs yield retrievals that are precise to within ∼2% at 30 m s−1, which is a factor of 2 improvement over the NOAA Hurricane Research Division’s SFMR, and comparable to the precision found here for GPS dropwindsonde near-surface wind speeds. A small (1.6 m s−1), but statistically significant, overall high bias was found for independent SFMR measurements utilizing emissivity data not used for model function development. Across the range of measured wind speeds (10–70 m s−1), SFMR 10-s averaged wind speeds are within 4 m s−1 (rms) of the dropwindsonde near-surface estimate, or 5%–25% depending on speed. However, an analysis of eyewall peak wind speeds indicates an overall 2.6 m s−1 GPS low bias relative to the peak SFMR estimate on the same flight leg, suggesting a real increase in the maximum wind speed estimate due to SFMR’s high-density sampling. Through a series of statistical tests, the SFMR is shown to reduce the overall bias in the peak surface wind speed estimate by ∼50% over the current flight-level wind reduction method and is comparable at extreme wind speeds. The updated model function is demonstrated to behave differently below and above the hurricane wind speed threshold (∼32 m s−1), which may have implications for air–sea momentum and kinetic energy exchange. The change in behavior is at least qualitatively consistent with recent laboratory and field results concerning the drag coefficient in high wind speed conditions, which show a fairly clear “leveling off” of the drag coefficient with increased wind speed above ∼30 m s−1. Finally, a composite analysis of historical data indicates that the earth-relative SFMR peak wind speed is typically located in the hurricane’s right-front quadrant, which is consistent with previous observational and theoretical studies of surface wind structure.

scholarly journals Robustness of the Recent Global Atmospheric Reanalyses for Antarctic Near-Surface Wind Speed Climatology

2020 ◽  
Vol 33 (10) ◽  
pp. 4027-4043 ◽  
Author(s):  
Xu Dong ◽  
Yetang Wang ◽  
Shugui Hou ◽  
Minghu Ding ◽  
Baoling Yin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Regional Scale ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Southern Annular Mode ◽  
Department Of Energy ◽  
Japan Meteorological Agency ◽  
Near Surface ◽  
Wind Speeds ◽  
Environmental Prediction

AbstractNear-surface wind speed observations from 30 manned meteorological stations and 26 automatic weather stations over the Antarctic Ice Sheet are used to examine the robustness of wind speed climatology in six recent global reanalysis products: the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Japan Meteorological Agency 55-Year Reanalysis (JRA-55), the Climate Forecast System Reanalysis (CFSR), the National Centers for Environmental Prediction–U.S. Department of Energy (DOE) Reanalysis 2 (NCEP2), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and fifth-generation reanalysis (ERA5). Their skills for representing near-surface wind speeds vary by season, with better performance in summer than in winter. At the regional scale, all reanalysis datasets perform more poorly for the magnitude, but better for their year-to-year changes in wind regimes in the escarpment than the coastal and plateau regions. By comparison, ERA5 has the best performance for the monthly averaged wind speed magnitude and the interannual variability of the near-surface wind speed from 1979 onward. Intercomparison exhibits high and significant correlations for annual and seasonal wind speed Antarctic-wide averages from different datasets during their overlapping timespans (1980–2018), despite some regional disagreements between the different reanalyses. Furthermore, all of the reanalyses show positive trends of the annual and summer wind speeds for the 1980–2018 period, which are linked with positive polarity of the southern annular mode.

scholarly journals The Analysis of the Spatiotemporal Variations and Mechanisms for the Near-Surface Wind Speed Over China in the Last 40 Years

2021 ◽  
Author(s):  
Xia Li ◽  
Yongjie Pan ◽  
Yingsha Jiang
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
The Other ◽  
Strong Wind ◽  
Reanalysis Dataset ◽  
Near Surface ◽  
Wind Speeds ◽  
Dispersion Degree ◽  
Weak Wind

Abstract Near-surface wind speed is of great significance in many aspects of the human production and living. This study analyses the spatiotemporal characteristics of the near-surface wind speed and wind speed percentiles with meteorological station observations in China from 1979 to 2019. Furthermore, the mechanisms of the wind speed variations are also investigated with ERA-Interim reanalysis dataset. Spatially, the wind speeds in the northern and eastern regions of China are larger than that in the central and southern regions. Seasonally, the wind speed in spring is significantly larger than that in the other seasons. The dispersion degree of wind speed in spring is larger than that in the other seasons both spatially and temporally. The near-surface wind speed in China shows significantly decreasing trends during 1979–2019, particularly in 1979–1999, but the wind speed trend reversed after 2000. After dividing the wind speed into different percentiles, it recognizes that the decreasing trend of stronger winds are more significant than that of weaker winds. The weaker the wind speed, the more significant increasing trend after 2000. Therefore, the decreasing wind speed trend before 2000 is mainly caused by the significant reduction of strong wind, while the reversal trend after 2000 results from the increase of weak wind. The variations of the wind speed over China attributed to both the U and V wind components, and the variations of zonal wind is closely related to the weakened upper westerly wind field and the uneven warming between high and low latitudes.

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.

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