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.

The seasonal variations in the significant wave height and sea surface wind speed of the China's seas

Long-term variations in a sea surface wind speed （WS） and a significant wave height （SWH） are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China＇s seas WS and SWH are determined based on 24 a （1988-2011） cross-calibrated, multi-platform （CCMP） wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III （WW3） wave model forced by CCMP wind data. The results show the following. （1） For the past 24 a, the China＇s WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/（s.a） in the WS, 1.3 cm/a in the SWH. （2） As a whole, the increasing trend of the China＇s seas WS and SWH is strongest in March-April-May （MAM） and December-January-February （DJF）, followed by June-July-August （JJA）, and smallest in September-October-November （SON）. （3） The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China＇s seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. （4） The variations in China＇s seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.

A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer

A scanning microwave radiometer（RM） was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square（RMS） difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

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 HHpolarized 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.

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-2 A 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.

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.

Retrieval of Sea Surface Wind Speed by One-Dimensional Synthetic Aperture Microwave Radiometer

One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0°65°.We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9,10.65,18.7,23.8 and 36.5 GHz.Then,the microwave radiative transfer forward model is used to simulate the measured brightness temperatures.The sensitivity of the brightness temperatures at 0°65°to the sea surface wind speed is calculated.Then,vertical polarization channels(VR),horizontal polarization channels(HR)and all channels(AR)are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0°65°,and the relationship between the retrieval error and incidence angle is obtained.The results are as follows:(1)The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization.(2)The retrieval error increases with Gaussian noise.The retrieval error of VR first increases and then decreases with increasing incidence angle,the retrieval error of HR gradually decreases with increasing incidence angle,and the retrieval error of AR first decreases and then increases with increasing incidence angle.(3)The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR.

Reliability ensemble averaging reduces surface wind speed projection uncertainties in the 21st century over China

Accurate prediction of future surface wind speed(SWS)changes is the basis of scientific planning for wind turbines.Most studies have projected SWS changes in the 21st century over China on the basis of the multi-model ensemble(MME)of the 6th Coupled Model Intercomparison Project(CMIP6).However,the simulation capability for SWS varies greatly in CMIP6 multi-models,so the MME results still have large uncertainties.In this study,we used the reliability ensemble averaging(REA)method to assign each model different weights according to their performances in simulating historical SWS changes and project the SWS under different shared socioeconomic pathways(SSPs)in 2015-2099.The results indicate that REA considerably improves the SWS simulation capacity of CMIP6,eliminating the overestimation of SWS by the MME and increasing the simulation capacity of spatial distribution.The spatial correlations with observations increased from 0.56 for the MME to 0.85 for REA.Generally,REA could eliminate the overestimation of the SWS by 33%in 2015-2099.Except for southeastern China,the SWS generally decreases over China in the near term(2020-2049)and later term(2070-2099),particularly under high-emission scenarios.The SWS reduction projected by REA is twice as high as that by the MME in the near term,reaching-4%to-3%.REA predicts a larger area of increased SWS in the later term,which expands from southeastern China to eastern China.This study helps to reduce the projected SWS uncertainties.

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.

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.

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.

Evolution of Surface Sensible Heat over the Tibetan Plateau Under the Recent Global Warming Hiatus

Based on regular surface meteorological observations and NCEP/DOE reanalysis data, this study investigates the evolution of surface sensible heat（SH） over the central and eastern Tibetan Plateau（CE-TP） under the recent global warming hiatus. The results reveal that the SH over the CE-TP presents a recovery since the slowdown of the global warming. The restored surface wind speed together with increased difference in ground-air temperature contribute to the recovery in SH.During the global warming hiatus, the persistent weakening wind speed is alleviated due to the variation of the meridional temperature gradient. Meanwhile, the ground surface temperature and the difference in ground-air temperature show a significant increasing trend in that period caused by the increased total cloud amount, especially at night. At nighttime, the increased total cloud cover reduces the surface effective radiation via a strengthening of atmospheric counter radiation and subsequently brings about a clear upward trend in ground surface temperature and the difference in ground-air temperature.Cloud–radiation feedback plays a significant role in the evolution of the surface temperature and even SH during the global warming hiatus. Consequently, besides the surface wind speed, the difference in ground-air temperature becomes another significant factor for the variation in SH since the slowdown of global warming, particularly at night.

An ocean current inversion accuracy analysis based on a Doppler spectrum model

Microwave remote sensing is one of the most useful methods for observing the ocean parameters. The Doppler frequency or interferometric phase of the radar echoes can be used for an ocean surface current speed retrieval,which is widely used in spaceborne and airborne radars. While the effect of the ocean currents and waves is interactional. It is impossible to retrieve the ocean surface current speed from Doppler frequency shift directly. In order to study the relationship between the ocean surface current speed and the Doppler frequency shift, a numerical ocean surface Doppler spectrum model is established and validated with a reference. The input parameters of ocean Doppler spectrum include an ocean wave elevation model, a directional distribution function, and wind speed and direction. The suitable ocean wave elevation spectrum and the directional distribution function are selected by comparing the ocean Doppler spectrum in C band with an empirical geophysical model function（CDOP）. What is more, the error sensitivities of ocean surface current speed to the wind speed and direction are analyzed. All these simulations are in Ku band. The simulation results show that the ocean surface current speed error is sensitive to the wind speed and direction errors. With VV polarization, the ocean surface current speed error is about 0.15 m/s when the wind speed error is 2 m/s, and the ocean surface current speed error is smaller than 0.3 m/s when the wind direction error is within 20° in the cross wind direction.

Physical mechanism and numerical simulations of surface layer temperature inversion in tropical ocean

The one-dimensional Kraus- Turner mixed layer model improved by Liu is developed to consider the effect of salinity and the equa- tions of temperature and salinity under the mixed layer. On this basis, the processes of growth and death of surface layer temperature inversion is numerically simulated under different environmental parameters. At the same time, the physical mechanism is preliminari- ly discussed combining the observations at the station of TOGA- COARE 0°N, 156°E. The results indicate that temperature inversion sensitively depends on the mixed layer depth, sea surface wind speed and solar shortwave radiation, etc., and appropriately meteoro- logical and hydrological conditions often lead to the similarly periodical occurrence of this inversion phenomenon.

Has the stilling of the surface wind speed ended in China?

Since the 1960 s,the global land surface wind speed(SWS)has significantly weakened,a phenomenon known as global terrestrial stilling.The latest research found that the stilling reversed around 2010,and since then the global SWS has been strengthening.However,there is still a lack of systematic quantitative analysis in China.We analyzed the transition and regional differences in the long-term trends of the SWS in China based on observational SWS data from 1971 to 2019.The results showed that annual mean SWS in China underwent a reversal from a continuously weakening trend to a significantly strengthening trend around 2014 and implying that stilling may have ended in 2014.The reversal had obvious regional and seasonal variations.In Northeast China,Western Xinjiang as well as on the Tibetan Plateau,the years with both annual and seasonal mean SWS changing from weakening to strengthening were around 2013/2014,1993/1994,and 2000.However,in the west of North China,SWS showed an obviously strengthening trend only in autumn and winter after 2007;while only autumn mean SWS showed a strengthening trend after 2012 in South China.It should be noted that stilling is ongoing in the eastern and southern coastal areas,North China and Eastern Xinjiang.

Quantifying and adjusting the impact of urbanization on the observed surface wind speed over China from 1985 to 2017

The observed surface wind speed(SWS)has declined across China over the last four decades,but the mechanisms responsible for this decline have been explored without reaching a consensus.In this study,we develop a physical method to quantify and adjust for the impact of urbanization around weather stations on the observed SWS over China from 1985 to 2017.The urbanization impact factor on the SWS is calculated based on Monin-Obukhov similarity theory,and the aerodynamic roughness length and zero-plane displacement height at each weather station are calculated yearly based on a 30-meter resolution satellite land cover product.The results show that urbanization around weather stations reduces the observed SWS by 11%on average over China.The urbanization impact on the observed SWS is the highest in southeastern China at 19%and the lowest over the Tibetan Plateau at 4%.Urbanization decreases the observed SWS by 9%over northwestern China and by 12%over northeastern China,northern China and southern China.More importantly,the proposed method can easily adjust for the urbanization impact on the observed SWS.After adjustment,the SWS appears to have started recovering during the 1990s,and the decreasing trend of SWS during the study period is nearly zero.The results shown here indicate that the observed decreasing trend of SWS from 1985 to 2017 over China is an observational local bias and does not reflect large-scale climatic variation.This inference is also consistent with geostrophic wind theory predictions;i.e.,SWS exhibits strong decadal variability,but its long-term trend is negligible.