This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effe...This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.展开更多
The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movem...The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.展开更多
In this paper,the efect of geographical location on Cyclone Global Navigation Satellite System(CYGNSS)observables is demonstrated for the frst time.It is found that the observables corresponding to the same wind speed...In this paper,the efect of geographical location on Cyclone Global Navigation Satellite System(CYGNSS)observables is demonstrated for the frst time.It is found that the observables corresponding to the same wind speed vary with geographic location regularly.Although latitude and longitude information is included in the conventional method,it cannot efectively reduce the errors caused by geographic diferences due to the non-monotonic changes of observables with respect to latitude and longitude.Thus,an improved method for Global Navigation Satellite System Refectometry(GNSS-R)wind speed retrieval that takes geographical diferences into account is proposed.The sea surface is divided into diferent areas for independent wind speed retrieval,and the training set is resampled by considering high wind speed.To balance between the retrieval accuracies of high and low wind speeds,the results with the random training samples and the resampling samples are fused.Compared with the conventional method,in the range of 0–20 m/s,the improved method reduces the Root Mean Square Error(RMSE)of retrieved wind speeds from 1.52 to 1.34 m/s,and enhances the correlation coefcient from 0.86 to 0.90;while in the range of 20–30 m/s,the RMSE decreases from 8.07 to 4.06 m/s,and the correlation coefcient increases from 0.04 to 0.45.Interestingly,the SNR observations are moderately correlated with marine gravities,showing correlation coefcients of 0.5–0.6,which may provide a useful reference for marine gravity retrieval using GNSS-R in the future.展开更多
Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the compl...Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface.Introducing redundant ancillary data may also result in over-reliance problems.Therefore,we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS(CYGNSS)obser-vations and Soil Moisture Active and Passive(SMAP)product.Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP,we establish five models for each grid with different parameters to achieve global SM retrieval.Subsequently,an optimal model,determined by the performance indicator,is used for SM retrieval.The results show that the root mean square error SRMsE with the improved methodis decreased by 9.1%using SMAP SM as reference with the SRMsE=0.040 cm^(3)/cm^(3) compared with using single reflectivity-temperature-vegetation method.Additionally,using the in-situ SM of International Soil Moisture Network as reference,the overall correlation coeffcient R and SRMSE values with the improved method are 0.80 and 0.064 cm^(3)/cm^(3),respectively.The average R of the chosen sites is increased by 22.7%,and the average SRMse is decreased by 8.7%.The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.展开更多
基金supported by the Funds for Creative Research Groups of China[Grant no.41721003]the National Natural Science Foundation of China[Grant nos.41825009 and 41774034].
文摘This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.
基金supported by the Guangxi Natural Science Foundation of China (2020GXNSFBA297145,Guike AD23026177)the Foundation of Guilin University of Technology(GUTQDJJ6616032)+3 种基金Guangxi Key Laboratory of Spatial Information and Geomatics (21-238-21-05)the National Natural Science Foundation of China (42064002,42004025,42074035,42204006)the Innovative Training Program Foundation (202210596015,202210596402)the Open Fund of Hubei Luojia Laboratory(gran 230100020,230100019)。
文摘The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.
基金the National Natural Science Foundation of China(Grant No.42074029)the Fund for Creative Research Groups of China(Grant No.41721003)the Natural Science Foundation of Hubei Province for Distinguished Young Scholars(Grant No.2021CFA039).
文摘In this paper,the efect of geographical location on Cyclone Global Navigation Satellite System(CYGNSS)observables is demonstrated for the frst time.It is found that the observables corresponding to the same wind speed vary with geographic location regularly.Although latitude and longitude information is included in the conventional method,it cannot efectively reduce the errors caused by geographic diferences due to the non-monotonic changes of observables with respect to latitude and longitude.Thus,an improved method for Global Navigation Satellite System Refectometry(GNSS-R)wind speed retrieval that takes geographical diferences into account is proposed.The sea surface is divided into diferent areas for independent wind speed retrieval,and the training set is resampled by considering high wind speed.To balance between the retrieval accuracies of high and low wind speeds,the results with the random training samples and the resampling samples are fused.Compared with the conventional method,in the range of 0–20 m/s,the improved method reduces the Root Mean Square Error(RMSE)of retrieved wind speeds from 1.52 to 1.34 m/s,and enhances the correlation coefcient from 0.86 to 0.90;while in the range of 20–30 m/s,the RMSE decreases from 8.07 to 4.06 m/s,and the correlation coefcient increases from 0.04 to 0.45.Interestingly,the SNR observations are moderately correlated with marine gravities,showing correlation coefcients of 0.5–0.6,which may provide a useful reference for marine gravity retrieval using GNSS-R in the future.
基金supported by Natural Science and Technology Planning Foundation of Guangxi (guikeAD23026257)the National Natural Science Foundation of China (42064002 and 42074029)and the“Ba Gui Scholars”program of the provincial government of Guangxi。
文摘Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface.Introducing redundant ancillary data may also result in over-reliance problems.Therefore,we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS(CYGNSS)obser-vations and Soil Moisture Active and Passive(SMAP)product.Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP,we establish five models for each grid with different parameters to achieve global SM retrieval.Subsequently,an optimal model,determined by the performance indicator,is used for SM retrieval.The results show that the root mean square error SRMsE with the improved methodis decreased by 9.1%using SMAP SM as reference with the SRMsE=0.040 cm^(3)/cm^(3) compared with using single reflectivity-temperature-vegetation method.Additionally,using the in-situ SM of International Soil Moisture Network as reference,the overall correlation coeffcient R and SRMSE values with the improved method are 0.80 and 0.064 cm^(3)/cm^(3),respectively.The average R of the chosen sites is increased by 22.7%,and the average SRMse is decreased by 8.7%.The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.
