Abstract:Guilin's ground-based GNSS station cannot be fully exploited in Precipitable Water Vapor (PWV) monitoring,owing to its omission of meteorological sensors.Here we use the pressure and temperature data from ERA5 reanalysis as well as from Guilin's ground meteorological stations to retrieve the GNSS PWV,so as to evaluate the accuracy and applicability of ERA5 in GNSS PWV retrieval in Guilin.The results show that referenced with data of Guilin's 10 ground meteorological stations in 2017,the ERA5 data of surface pressure (annual bias:-0.35 hPa,annual RMSE:0.65 hPa) and temperature (annual bias:0.86 K,annual RMSE:1.66 K) are accurate enough for GNSS PWV retrieval.As for the GNSS PWV retrieval results,the ERA5 approach has an average bias of 0.17 mm and RMSE of 0.35 mm,which is well correlated and consistent with meteorological station approach.The above results validate the accuracy and applicability of ERA5 surface temperature and pressure products in GNSS PWV retrieval in Guilin,and provide reference for GNSS PWV retrieval and data selection in other regions.

Abstract:The key parameters including air pressure,air temperature,water vapor weighted mean temperature (T_m) and Zenith Total Delay (ZTD),which are involved in GNSS precipitable water vapor retrieval,are estimated based on CRA40 released by China Meteorological Administration.The accuracy and reliability of the estimated key parameters over China are comprehensively evaluated,taking data of ground meteorological station,radiosonde station and GNSS station as reference.The performance of CRA40 is compared with the latest ECMWF global reanalysis,ERA5.The results illustrate that CRA40 performs slightly worse than ERA5 in aspects of air pressure and ZTD,with average Root Mean Squares (RMS) of 0.91 hPa and 13.5 mm,respectively.While CRA40 is generally superior to ERA5 in air temperature and T_m estimation,with RMS of 2.67 K and 1.47 K,respectively.The diurnal variations of three parameters including air pressure,air temperature and ZTD estimated from CRA40 generally agree well with the in-situ measurements.

Abstract:The Global Pressure and Temperature (GPT) series models can be used to calculate temperature,pressure,water vapor pressure and other meteorological parameters for any area in the world.At present,GPT2w is the most widely used global pressure and temperature model with high precision.In this paper,the accuracy of GPT2w model for China's mainland areas was analyzed by comparison with the measured data of temperature,pressure and water vapor pressure from 102 national meteorological stations during 2012 to 2016.The results showed that the mean error and mean standard deviation of GPT2w temperature were -0.45 ℃ and 10.04 ℃,respectively.The mean error and mean standard deviation of pressure were 2.05 hPa and 6.55 hPa.While for the water vapor pressure,the mean error was 0.11 hPa and the mean standard deviation was 6.15 hPa.In general,the GPT2w can calculate temperature,pressure,and water vapor pressure with high accuracy for most of China's mainland areas,though the accuracy varies between different areas,latitudes and seasons.

Abstract:MODIS Precipitable Water Vapor (PWV) has high spatial resolution but is easily affected by environmental factors such as clouds and rain,and its accuracy is not high.GNSS PWV has low spatial resolution but has advantages of all-weather,weather-free,and high accuracy.Previous researches have shown a significant linear correlation between the two.Combining the advantages of both,correcting MODIS PWV based on GNSS PWV data can obtain PWV for large area with high precision.Yet traditional linear regression correction model does not take into account the deterioration of the linear correlation due to influences from environmental factors such as clouds and aerosols.To address this,a non-linear term of Day of Year is added to traditional linear model thus construct a new correction model.The GNSS tropospheric delay and MODIS near-infrared data in Hong Kong during 2017-2019 are used to verify the proposed correction model,and the result of linear residual spectrum shows a significant annual cycle.Compared with traditional correction model,the new model is significantly improved in aspects of average absolute error,average relative error,root mean square error,and fitting,which verifies the new model in feasibility and accuracy for MODIS PWV correction.

Abstract:We used the ERA5 data from European Centre for Medium-Range Weather Forecasts to evaluate the accuracy of regional weighted mean temperature (T_m) linear models over China.These models were derived from historical radiosonde data of 88 Chinese stations.For these stations,the root mean square errors (RMSEs) between single-station model derived T_m and ERA T_m are 1.8-5.5 K,and -1.22-4.54 K of which are system biases.For 82 out of the total 88 stations,the system biases are positive,indicating that the model derived values are generally larger than ERA T_m.The RMSEs can be reduced to 1.5-3.5 K after removal of the system biases.On average,the RMSE of T_m from single-station model is smaller than that from the unified model by 0.6 K,suggesting that single-station models are generally more accurate than the unified model.In western China,northwestern China and Inner Mongolia,the accuracy can be improved by 1-3.9 K using single station models compared with the unified model.Time series of the biases between model derived T_m and ERA T_m show obvious seasonal cycles for over half of the stations.

Abstract:Summer rainstorms occur frequently in Beijing-Tianjin-Hebei region.As one of the key factors affecting the formation of heavy rain,precipitable water vapor has been recognized as highly correlated with the Global Navigation Satellite Systems Zenith Tropospheric Delay(GNSS ZTD).In this study,the time series of GNSS ZTD and their response to El Niño events were analyzed based on GNSS observations in Beijing-Tianjin-Hebei region.Fast Fourier transform and wavelet transform were used to carry out GNSS ZTD timing sequence analysis from frequency domain and time domain,and the time sequence of GNSS ZTD with different periodicity was compared with the Eastern-Pacific types of El Niño index(I_EP) and the Central-Pacific types of El Niño index(I_CP).The results showed that there was a corresponding relationship between the abnormal period of GNSS ZTD and El Niño events.Specifically,the GNSS ZTD was positively correlated with I_EP,and significantly negatively correlated with I_CP.Under the influence of I_EP,the GNSS ZTD increased in seasonal cycle,but decreased in monthly cycle and half-monthly cycle.While under the influence of I_CP,the GNSS ZTD decreased in seasonal cycle,monthly cycle,and half-monthly cycle.The above results can provide reference for regional GNSS ZTD variation analysis,and provide a reliable basis for sensing El Niño events with precipitable water vapor.

Abstract:Precise point positioning (PPP) has been well established for retrieving the precipitable water vapor (PWV) in the static model.Yet it is not always suitable for static PPP PWV retrieving,for an instance,the GNSS antenna keeps moving.Position and zenith total delay (ZTD) are coinstantaneously estimated in dynamic PPP model,which is an option for the PWV retrieving in dynamic scenario.GNSS data over 3.5 hours have been gathered from the Continuously Operating Reference Stations (CORS).Both static and dynamic PPPs have been applied for the CORS data processing.The result reveals that the maximum difference is 6.6 mm,and dynamic PPP PWV graduate movement agrees in magnitude with that from static PPP PWV.A rotation arm has been designed for GNSS receiver gathering data in dynamic scenario.PPP in dynamic model can retrieve the rotation arm PWV,which makes sense to the one from CORS PWV,but lose the capacity of detecting the PWV changes.GNSS station can have displacement in a short term in the earthquake event.Both static PPP and dynamic PPP have been assessed for this scenario.The result reveals that displacement due to the earthquake does not make an evident effect on the PWV retrieving both in static PPP and dynamic PPP.The static PPP is still recommended for the PWV retrieving over the earthquake period.

Abstract:The hydrological changes of the Yellow River are complex,and the existing water level monitoring methods are time-consuming and laborious and cannot well meet the requirements of both time resolution and spatial resolution.The emergence of ground-based GPS-IR technology provides a new approach for water level monitoring,but the receivers currently used in most ground-based GPS-IR technologies are high-precision geodetic receivers,which are costly and weak in timeliness thus are not suitable for wide application.This article first introduces the composition of the universal GPS receiver and analyzes the quality of GPS observation data,then gives out the basic principle of ground-based GPS-IR technology for water level monitoring.Finally,the GPS-IR water level data are extracted from GPS data measured by the Yellow River Yanguo Gorge,which are then compared with the water level monitoring data of Lanzhou Hydrometric Station.The experimental results show that the water levels retrieved by universal GPS receiver are accurate with the best RMSE being 0.21 m,verifying the applicability of universal GPS receiver for long-term water level monitoring.

Abstract:In this paper,a new scheme is proposed for simultaneously retrieving Soil Moisture (SM) and Vegetation Optical Depth (VOD),solely from the Cyclone Global Navigation Satellite System (CyGNSS) data.This work is accomplished by employing two pre-trained neural networks (NNs),including one for computing SM from the CyGNSS data and Soil Moisture Active Passive (SMAP) VOD as well as the other for calculating VOD from the CyGNSS data and SMAP SM product,through a brute-force searching.By adopting the proposed method,the posterior SM/VOD can be estimated merely using the CyGNSS data,free from other auxiliary data.The attained results are validated against SMAP products for two separate periods:the whole year of 2018 and a recent course in 2020.Satisfactory agreements between the retrieved and referred SM/VOD are achieved,with correlation coefficients (r) of 0.86 and 0.84,along with root-mean-square errors (RMSEs) of 0.064 and 0.071 cm³/cm³ for SM in the years of 2018 and 2020,respectively;and for the verification of VOD,r=0.98 and RMSE=0.079 are acquired for 2018,and r=0.98 and RMSE=0.084 for 2020,respectively.The good consistency obtained in this work illustrates the capability of CyGNSS as a new independent source for estimating pan-tropical SM and VOD.

Abstract:The retrieval of tropospheric and ionospheric parameters from global and regional Global Navigation Satellite System (GNSS) reference station networks has the benefits of low cost,high precision and real-time.The GNSS space environmental monitoring platform of Xsensing proposed by Nanjing University of Information Science and Technology is introduced in this paper.Firstly,the accuracy of tropospheric parameter inversion under different meteorological conditions is evaluated with the tropospheric products provided by the international GNSS service as a reference.Then,the ambiguity-fixed Precise Point Positioning (PPP) method is used to realize high-precision ionospheric parameter inversion.The experimental results show that the mean bias of the tropospheric delay errors is 0.05 mm,and the corresponding standard deviation is 5.6 mm.The results can reflect the trend of water vapor changes with high accuracy under stable or severe weather conditions.The accuracy of ionospheric inversion is evaluated based on a short baseline test.The mean bias of ionospheric delay errors from the ambiguity-float PPP is -0.09 TECU,and the accuracy is 0.38 TECU.The ambiguity fixed PPP method can improve the accuracy by 84.2％.Besides,comparable accuracy can be achieved using the static and simulated kinematic PPP.The results verify the initial accuracy of the Xsensing platform and pave the way for improving the spatial and temporal resolution of the space environment monitoring using airborne and shipborne platforms.

Abstract:With the Total Electron Content (TEC)-based Electron Density Profile (EDP) inversion method,the phase data of Radio Occultation (RO) events of Constellation Observing System for Meteorology,Ionosphere and Climate (COSMIC) and Fengyun-3C (FY-3C) missions are processed.The inverted EDPs and ionospheric peak parameters are compared with the official products of the two RO missions,respectively.It is found that although good consistencies exist between the inverted results and the official products for both of the two missions,the consistency is more significant for COSMIC mission.Besides,for both of the two RO missions,the consistence between the inverted NmF2 values and the official products is better than that between the inverted hmF2 values and the official products.There are some differences in detailed inversion processes between the two RO missions.First,it is necessary to smooth the phase data before the EDP inversion process for FY-3C mission,which is not the case for COSMIC mission.Second,although TEC correction is effective for COSMIC RO EDP inversions,it is not a recommended practice in FY-3C EDP inversion process due to the non-availiability of enough non-RO period phase data in FY-3C RO mission.

Abstract:In order to compare the forecast performance of different time series models,the carrier phase smoothing pseudo range method is used to calculate the ionospheric Vertical Total Electron Content (VTEC) over a single station under the condition of calm ionosphere.The Auto Regressive Integrated Moving Average (ARIMA) model and Holt Winters exponential smoothing model are used for station-by-station modeling.A 3-day forecast is achieved through a 9-day sample sequence,and the forecast values are systematically evaluated.The results show that the time series models can well reflect the change in ionospheric VTEC during the forecast period,and the mean square root errors are not more than 5 TECU.In addition,the Holt-Winters multiplicative model has the largest deviation in the forecast value,followed by the additive model.The ARIMA model has higher relative accuracy at the 11 stations than the Holt-Winters exponential smoothing model,and has the smallest root mean square error peak as well as the highest forecast accuracy.

Abstract:At present,research on smartphone GNSS high-precision positioning observations mainly focuses on quality analysis and positioning algorithms,but few studies have been carried out on their atmospheric applications.In this research,the ionospheric delay using the raw GNSS dual-frequency carrier phase observations from Xiaomi Mi 8,the world’s first dual-frequency smartphone,is estimated and compared with that of Trimble Alloy geodesic receiver with ultra-short baseline.The quality of observations is controlled by considering the small ionospheric variation between successive epochs.The results show that the observations of smartphone may still be invalid when the satellite elevation or the carrier-to-noise is high.The method of setting threshold of satellite elevation or the carrier-to-noise is no longer applicable for quality control.The ionospheric delay retrieved by Xiaomi Mi 8 is in good agreement with that of Trimble Alloy geodetic receiver,with slightly higher fluctuation range.The retrieving accuracy is better than 0.2 TECU,indicating that the GNSS observations from smartphone is feasible for atmospheric application.

Abstract:In order to improve the accuracy of numerical precipitation forecasting,forecast data from the European Centre for Medium-range Weather Forecasts (ECMWF)and Jiangxi meteorological observation stations were used in this study.A gamma function was used to simulate the precipitation cumulative probability of prediction and observation.The method was tested for a precipitation concentration period in Jiangxi province in 2017.The results show that the ECMWF forecast-observed precipitation probability matching dynamic correction method provides the latest real-time forecasts and observations for probability matching,and updates the precipitation correction values of all grades automatically according to the prediction adjustment and error of the recent model.It is found that the ECMWF's daily 12 h interval precipitation forecast is generally larger for precipitation 10 mm and below,and smaller for precipitation 25 mm and above.The precipitation forecast in the Jiujiang area along the Yangtze River and Jingdezhen is close to actual conditions.This precipitation forecasting method can improve the threat score of light and heavy rain,reduces the PO of heavy rain,and increases its POD.However,the correction effect of heavy rain and some moderate rain is not good;hence,the advantages and disadvantages should be considered in practice.

Abstract:Climate change in the arid area of Xinjiang is obvious in recent years.Whether there is a threshold value for grassland ecosystem response to atmospheric nitrogen deposition and climate change in this area needs to be further studied.This paper takes the four types of grassland (alpine meadow (AM),mid-mountain forest-meadow (MMFM),low-mountain dry grassland (LMDG) and plain desert grassland (PDG)) in the northern slopes of Tianshan Mountain as research object,based on the DNDC model,to reveal the influence of atmospheric nitrogen deposition and climate change on the grassland net primary productivity (NPP).The results showed that:1) there was a threshold value of NPP response to nitrogen deposition,and the response thresholds of PDG,LMDG,MMFM,and AM were 20±5.77,60±26.46,50±15.28,and 30±11.55 kg·hm^-2,respectively;2) the hydrothermal condition was the main factor determining NPP;3) there was a threshold value of NPP response to temperature rise in PDG grassland,while no definite conclusion had been drawn for the other types in the current study;4) the NPP of PDG and LMDG grassland was positively correlated with precipitation,while the change of AM grassland NPP was negatively correlated with precipitation.The sensitivity of different grassland types to precipitation changes also varied greatly,with PDG being the largest,followed by LMDG,AM,and MMFM.

Abstract:Aerosol column single scattering albedos over the Yangtze Delta region were analyzed to study monthly,seasonal,and yearly variations.Daily Ozone Monitoring Instrument (OMI) satellite data for the period between January 1,2006 and December 31,2017 was used for this study.The single scattering albedo was measured for the wavelength of 483.5 nanometers.The area-averaged time series albedo map revealed that the albedo is generally higher over the ocean than inland.The daily aerosol column single scattering albedo is range from 0.881 to 0.971,with a mean value of 0.939±0.024.The maximum distribution probability lies in the range of 0.965-0.970 with a probability of 25％.The annual mean aerosol column single scattering albedos are in the range of 0.938-0.940,with narrow variations.The monthly average single scattering albedos vary from the lowest value of 0.915 during February to the highest of 0.968 during June,July,and August.From a seasonal perspective,single scattering albedo is lowest in winter (0.919±0.001),moderate in spring (0.929±0.002) and fall (0.941±0.003),and maximum in summer (0.968±0.001).