Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Role of Downward Momentum Transport in the Formation of Severe Surface Winds

The Weather Research and Forecasting (WRF) model was used to investigate the role of downward momentum transport in the formation of severe surface winds for a squall line on 3-4 June 2009 across regions of the Henan and Shandong Provinces of China. The results show that there was a strong westerly jet belt with a wind speed greater than 30 m s 1 and a thickness of 5 km at an altitude of 11-16 km. The jet belt was accelerated, and it descended while the squall line convective system occurred. It was found that the appearance of strong negative perturbation pressure accompanied by the squall line caused the acceleration of the upper-level westerly jet and increased the horizontal wind speed by a maximum of 18%. Meanwhile, the negative buoyancy due to the loading, melting, and evaporation of cloud hydrometeors induced the downward momentum transport from the upper levels. The downward momentum transport contributed approximately 70% and the surface cold pool 30% to the formation of severe surface winds.

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

Sea surface winds from reanalysis （NCEP-2 and ERA-40 datasets） and satellite-based products （QuikSCAT and NCDC blended sea winds） are evaluated using in situ ship measurements from the Chinese National Antarctic Research Expeditions （CH1NAREs） from 1989 through 2006, with emphasis on the Southern Ocean （south of 45°S）. Compared with ship observations, the reanalysis winds have a positive mean bias （0.32 m·s-1 for NCEP-2 and 0.13 m·s-1 for ERA-40）, and this bias is more pronounced in the Southern Ocean （0.57 m·s-1 and 0.45 m·s-1, respectively）. However, mean biases are negative in the tropics and subtropics. The satellite-based winds also show positive mean biases, larger than those of the reanalysis data. All four wind products overestimate ship wind speed for weak winds （〈4 m·s-1） but underestimate for strong winds （〉10 m·s-1）. Differences between the reanalysis and satellite winds are examined to identify regions with large discrepancies.

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.

Chlorophyll a increase induced by surface winds in the northern South China Sea

The response of chlorophyll a （Chl a） concentration to wind stress is analyzed in the South China Sea （SCS）, using in-situ data of Chl a and remote sensing data （QuikScat-sea surface wind （SSW）, AVHRR-sea surface temperature （SST）, AVISO merged-sea level anomalies （SLA）, SeaWiFS- derived Chl a and MODIS Terra-derived Chl a） in August/September/October 2004, 2006 and 2009. The variability of SSW, SST and SLA 7 d before in-situ Chl a sampling （including the work day of in^situ Chl a sampling） with the same latitude and longitude of the study area are investigated, and the correlation coefficients are calculated between these hydrographic factors and in-situ Chl a concentration. The results show that the Chl a-SSW correlation coefficients at upper layers （such as 0 m and 25 m） are more significant than those at deeper layers （such as 50, 75 and 100 m） 1 3 d before, which indicates that there is a time lag of strong surface winds stimulating phytoplankton bloom. By analyzing the relationship among the daily remote sensing derived （RS- derived） SSW, SST, SLA and 3 d averaged SeaWiFS/MODIS-derived Chl a concentration in the northern SCS in September 2004 and 2009 respectively, it shows that the intensity and speed of surface winds could have great influence on extend of Chl a increase. If surface winds reach 4-5 m/s over, Chl a concentration would increase 1-3 d after the process of strong surface winds in open sea area of the northern SCS mainly during September.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Estimating tropical cyclone surface winds: Current status, emerging technologies, historical evolution, and a look to the future

This article provides a review of tropical cyclone(TC)surface wind estimation from an operational forecasting perspective.First,we provide a summary of operational forecast center practices and historical databases.Next,we discuss current and emerging objective estimates of TC surface winds,including algorithms,archive datasets,and individual algorithm strengths and weaknesses as applied to operational TC surface wind forecast parameters.Our review leads to recommendations about required surface coverage—an area covering at least 1100 km from center of TC at a 2-km resolution in the inner-core,and at a frequency of at least once every 6 h.This is enough coverage to support a complete analysis of the TC surface wind field from center to the extent of the 34-kt(17 m s^(-1))winds at 6-h intervals.We also suggest future designs of TC surface wind capabilities include funding to ensure near real-time data delivery to operators so that operational evaluation and use are feasible within proposed budgets.Finally,we suggest that users of archived operational wind radii datasets contact operational organizations to ensure these datasets are appropriate for their needs as the datasets vary in quality through time and space,even from a single organisation.

Impact of Air Pollution on Summer Surface Winds in Xi’an

By analysis of observation data, this paper demonstrates that pollution particles could reduce surface wind speed through blocking solar radiation to the ground. The comparation between temperature at the lowland meteorological station Xi’an and that over the nearby highland station Mt. Hua suggests that surface solar radiation at Xi’an is reduced due to the increasing anthropogenic aerosols. The reduced surface energy suppresses the atmospheric instability and convective flows, and thus the downward transfer of faster winds aloft is reduced. Consequently, wind speeds near surface are weakened. This reduction of surface winds is shown by the significant reverse trends of wind speeds over the two stations at different elevations. The aerosols’ effects on winds are also manifested in the trends of radionsonde wind speed. The decreased surface winds in Xi’an have also reduced local pan evaporation.

Comparison of sea surface wind field measured by HY-2A scatterometer and WindSat in global oceans

In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.

Evaluation of the Impact of Observations on Blended Sea Surface Winds in a Two-Dimensional Variational Scheme Using Degrees of Freedom

This paper presents an evaluation of the observational impacts on blended sea surface winds from a two- dimensional variational data assimilation （2D-Var） scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal （DFS）, and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two meth- ods, a priori and a posteriori, are used to estimate the DFS of the zonal （u） and meridional （v） components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is re- duced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.

Assimilation of Ocean Surface Wind Data by the HY-2B Satellite in GRAPES: Impacts on Analyses and Forecasts

The ocean surface wind(OSW)data retrieved from microwave scatterometers have high spatial accuracy and represent the only wind data assimilated by global numerical models on the ocean surface,thus playing an important role in improving the forecast skills of global medium-range weather prediction models.To improve the forecast skills of the Global/Regional Assimilation and Prediction System Global Forecast System(GRAPES_GFS),the HY-2B OSW data is assimilated into the GRAPES_GFS four-dimensional variational assimilation(4DVAR)system.Then,the impacts of the HY-2B OSW data assimilation on the analyses and forecasts of GRAPES_GFS are analyzed based on one-month assimilation cycle experiments.The results show that after assimilating the HY-2B OSW data,the analysis errors of the wind fields in the lower-middle troposphere(1000-600 hPa)of the tropics and the southern hemisphere(SH)are significantly reduced by an average rate of about 5%.The impacts of the HY-2B OSW data assimilation on the analysis fields of wind,geopotential height,and temperature are not solely limited to the boundary layer but also extend throughout the entire troposphere after about two days of cycling assimilation.Furthermore,assimilating the HY-2B OSW data can significantly improve the forecast skill of wind,geopotential height,and temperature in the troposphere of the tropics and SH.

First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval

Gaofen-3-02(GF3-02)is the first C-band synthetic aperture radar(SAR)satellite with terrain observation with progressive scans of SAR(TOPSAR)imaging mode in China,which plays an essential role in marine environment monitoring.Given the weak scattering characteristics of the ocean,the system thermal noise superimposed on SAR images has significant interference,especially in cross-polarization channels.Noise-Equivalent Sigma-Zero(NESZ)is a measure of the sensitivity of the radar to areas of low backscatter.The NESZ is defined to be the scattering cross-section coefficient of an area which contributes a mean level in the image equal to the signal-independent additive noise level.For TOPSAR,NESZ exhibits the shape of the SAR scanning gain curve in the azimuth and the shape of the antenna pattern in the range.Therefore,the accurate measurement of NESZ plays a vital role in the application of spaceborne SAR sea surface cross-polarization data.This paper proposes a theoretical calculation method for the NESZ curve in GF3-02 TOPSAR mode based on SAR noise inner calibration data and the imaging algorithm.A method for correcting the error existing in the theoretical curve of NESZ is also proposed according to the relationship between sea surface backscattering and wind speed and the same characteristics of target scattering in the overlapping area of adjacent sub-swaths.According to assessment with wide-swath TOPSAR cross-polarization data,the GF3-02 TOPSAR mode has a very low thermal noise level,which is better than−33 dB at the edge of each beam,and controlled below−38 dB at the center of the beam.The two-dimensional reference curves of the NESZ of each beam are provided to the GF3-02 TOPSAR users.After discussing the relationship between normalized radar cross section(NRCS)and wind speed,we provide a formula for NRCS related to wind speed and radar incidence angle.Compared with the NRCS derived from this formula and the NESZ-subtracted NRCS of SAR images,the bias is−0.0048 dB,the Root Mean Square Error is 1.671 dB and the correlation coefficient is 0.939.

Effect of sea surface wind on the seasonal variation of sea level in the east of China seas

Sea level seasonal variations in the east of China seas from 2004 to 2006 are simulated by the advanced ROMS model. The results show similar sea level spatial features with TOPEX/Poseidon observations, with annual ranges decreasing gradually from the sea coast to the Kuroshio region. By getting rid of wind stress in ROMS model, the simulated sea level results still show obvious seasonal variations. However, the phenomenon of sea level anomaly disappears in Min Zhe Current Coastwise （MZCF） and Su Bei current coastwise （SBCF）, and the change of it from coastal area to ocean recedes. The seal level difference between Bohai, Yellow Sea （BYS） and East China Sea （ECS） becomes weaker in spring and autumn. The annual differences decrease obviously, and the gradual change of annual ranges from seacoast to the Kuroshio almost disappears. The annual ranges in BYS are nearly identical. The annual range ratio without the wind stress to with the wind stress increases gradually from the sea coast to Kuroshio region.

Sea Surface Roughness Derivation from Wind Speed Estimated by Satellite Altimeter

For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.

Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.

Applications of AMSR-E Measurements for Tropical Cyclone Predictions PartⅠ: Retrieval of Sea Surface Temperature and Wind Speed

Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （AMSRoE） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSRoE measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.

An Assessment of the Quality of Surface Sensible Heat Flux Derived from Reanalysis Data through Comparison with Station Observations in Northwest China

The present study compares seasonal and interdecadal variations in surface sensible heat flux over Northwest China between station observations and ERA-40 and NCEP-NCAR reanalysis data for the period 1960-2000. While the seasonal variation in sensible heat flux is found to be consistent between station observations and the two reanalysis datasets, both land-air temperatures difference and surface wind speed show remarkable systematic differences. The sensible heat flux displays obvious interdecadal variability that is season-dependent. In the ERA-40 data, the sensible heat flux in spring, fall, and winter shows interdecadal variations that are similar to observations. In the NCEP-NCAR reanalysis data, sensible heat flux variations are inconsistent with and sometimes even opposite to observations. While surface wind speeds from the NCEP-NCAR reanalysis data show interdecadal changes consistent with station observations, variations in land-air temperature difference differ greatly from the observed dataset. In terms of land-air temperature difference and surface wind speed, almost no consistency with observations can be identified in the ERA-40 data, apart from the land-air temperature difference in fall and winter. These inconsistencies pose a major obstacle to the application in climate studies of surface sensible heat flux derived from reanalysis data.

Distinct Modes of Winter Arctic Sea Ice Motion and Their Associations with Surface Wind Variability

Using monthly mean sea ice velocity data obtained from the International Arctic Buoy Programme （IABP） for the period of 1979–1998 and the monthly mean NCEP/NCAR re-analysis dataset （1960–2002）, we investigated the spatiotemporal evolution of the leading sea ice motion mode （based on a complex correlation matrix constructed of normalized sea ice motion velocity） and their association with sea level pressure （SLP） and the predominant modes of surface wind field variability. The results indicate that the leading winter sea ice motion mode’s spatial evolution is characterized by two alternating and distinct sea ice modes, or their linear combination. One mode （M1） shows a nearly closed cyclonic or anti-cyclonic circulation anomaly in the Arctic Basin and its marginal seas, resembling to a large extent the response of sea ice motion to the Arctic Oscillation （AO）, as many previous studies have revealed. The other mode （M2） displays a coherent cyclonic or anti-cyclonic circulation anomaly with its center close to the Laptev Sea, which has not been identified in previous observational studies. In fact, M1 and M2 respectively reflect the responses of sea ice motion to two predominant modes of winter surface wind variability north of 70 ？ N, which well correspond, with slight differences, to the first two modes of EOF analysis of winter monthly mean SLP north of 70 ？ N. These slight differences in SLP anomalies lead to a difference of M2 from the response of sea ice motion to the dipole anomaly. Although the AO significantly influences sea ice motion, it is not crucial for the existence of M1. The new sea ice motion mode （M2） has the largest variance and clearly differs from the response of winter monthly mean sea ice motion to the dipole anomaly in SLP fields, and corresponding SLP anomalies also show differences compared to the dipole anomaly. This study indicates that in the Arctic Basin and its marginal seas, slight differences in SLP anomaly patterns can force distinctly different sea ice motion anomalies.

A Method for Sea Surface Wind Field Retrieval from SAR Image Mode Data

To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions （GMFs） have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by com-bining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval re-suits by a combination method （CMOD5 and CMOD4） together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error （RMSE） of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.