A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

Analysis of rain attenuation prediction models at Ku-band in Thailand

The performance of the existing rain attenuation models in tropical zones is still a debated issue due to the lack of measurements reported from these areas of the world to develop and validate prediction models. A three-year (2003–2005) campaign of rainfall rate and rain attenuation measurements was conducted on a satellite beacon link located in a tropical region of Thailand. The cumulative distributions of rain attenuation derived from the measured data are presented and compared with those obtained with existing prediction models.     

Impact of rain on wave retrieval from Sentinel-1 synthetic aperture radar images in tropical cyclones

The main objective of our work was to investigate the impact of rain on wave observations from C-band (~5.3 GHz) synthetic aperture radar (SAR) in tropical cyclones. In this study, 10 Sentinel-1 SAR images were available from the Satellite Hurricane Observation Campaign, which were taken under cyclonic conditions during the 2016 hurricane season. The third-generation wave model, known as Simulating WAves Nearshore (SWAN) (version 41.31), was used to simulate the wave fields corresponding to these Sentinel-1 SAR images. In addition, rainfall data from the Tropical Rainfall Measuring Mission satellite passing over the spatial coverage of the Sentinel-1 SAR images were collected. The simulated results were validated against significant wave heights (SWHs) from the Jason-2 altimeter and European Centre for Medium-Range Weather Forecasts data, revealing a root mean square error (RMSE) of ~0.5 m with a 0.25 scatter index. Winds retrieved from the VH-polarized Sentinel-1 SAR images using the Sentinel-1 Extra Wide-swath Mode Wind Speed Retrieval Model after Noise Removal were taken as prior information for wave retrieval. It was discovered that rain did indeed affect the SAR wave retrieval, as evidenced by the 3.21-m RMSE of SWHs between the SAR images and the SWAN model, which was obtained for the ~1000 match-ups with raindrops. The raindrops dampened the wave retrieval when the rain rate was < ~5 mm/hr; however, they enhanced wave retrieval for higher rain rates. It was also found that the portion of the rain-induced ring wave with a wave number > 0.05 rad/m (~125 m wavelength) was clearly observed in the SAR-derived wave spectra.     

Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan

Multi-sensor precipitation datasets including two products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and estimates from Climate Prediction Center Morphing Technique (CMORPH) product were quantitatively evaluated to study the monsoon variability over Pakistan. Several statistical and graphical techniques are applied to illustrate the nonconformity of the three satellite products from the gauge observations. During the monsoon season (JAS), the three satellite precipitation products captures the intense precipitation well, all showing high correlation for high rain rates (>30 mm/day). The spatial and temporal satellite rainfall error variability shows a significant geo-topography dependent distribution, as all the three products overestimate over mountain ranges in the north and coastal region in the south parts of Indus basin. The TMPA-RT product tends to overestimate light rain rates (approximately 100%) and the bias is low for high rain rates (about ±20%). In general, daily comparisons from 2005 to 2010 show the best agreement between the TMPA-V7 research product and gauge observations with correlation coefficient values ranging from moderate (0.4) to high (0.8) over the spatial domain of Pakistan. The seasonal variation of rainfall frequency has large biases (100–140%) over high latitudes (36N) with complex terrain for daily, monsoon, and pre-monsoon comparisons. Relatively low uncertainties and errors (Bias ±25% and MAE 1–10 mm) were associated with the TMPA-RT product during the monsoon-dominated region (32–35N), thus demonstrating their potential use for developing an operational hydrological application of the satellite-based near real-time products in Pakistan for flood monitoring.     

Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

Second order experimental statistics of rain attenuation at Ka band in a tropical location

Some second order rain attenuation statistics such as fade duration and fade slope are investigated on the basis of experimental measurements of received signals using the GSAT-14 satellite beacon signal at 20.2 GHz for three years (2014–2016) over the tropical location Ahmedabad (23.02 ⁰E, 72.51⁰N), India with an Elevation angle of 63⁰. Existing models of fade duration are compared with experimental data in this study and exponent of power law model of fade duration at Ka band is further explored. A new model for fade duration for Ka band for tropical locations is proposed where the constant of exponent of attenuation in the power law is found to be 0.143 instead of 0.055 used in ITU-R. Other relevant parameters for implementation of fade mitigation technique to prevent the link outage like cumulative distribution of signal fade rate, maximum and minimum fade rise and fade fall are also studied. Fade slope asymmetry over tropical region is also investigated. Keeping in view of exploiting the commercial launch of Ka band in Indian region there is an urgent need for validation of the existing models of fade slope (specially looking into fade symmetry) and fade duration. It will help the SATCOM (Satellite Communication) link designer to improve closed loop fade mitigation technique to minimize the possible link failure/link outage over the tropical region.     

Development and performance evaluation of tropospheric scintillation model on Ku-band satellite link over Akure,Nigeria

Existing amplitude scintillation prediction models often perform less satisfactorily when deployed outside the regions where they were formulated. This necessitates the need to evaluate the performance of scintillation models developed in one region using data data from other regions while documenting their relative errors. Due to its variation with elevation angle, frequency, other link parameters and meteorological factors, we employed three years (January 2016 to December 2018) of concurrently measured satellite radio beacons and tropospheric weather parameters to develop a location-based amplitude scintillation prediction model over the Earth-space path of Akure (7.17^oN, 5.18^oE), South-western Nigeria. The satellite beacon measurement used Tektronix Y400 NetTek Analyzer at 1 s integration time while meteorological parameters, namely; temperature, pressure and relative humidity were measured using Davis Vantage Vue weather station at 1 min integration time. Comparative study of the model’s performance with nine (9) existing scintillation prediction models indicates that the best and worst performing models, in terms of root mean square error (RMSE), are the Statistical Temperature and Refractivity (STN) and direct physical and statistical prediction (DPSP) models with values 11.48 and 51.03 respectively. Also, worst month analysis indicates that April, with respective enhancement and fade values of 0.88 and 0.90 dB for 0.01% exceedance, is the overall worst calendar month for amplitude scintillation.     

Investigation of vertical profile of rain microstructure at Ahmedabad in Indian tropical region

The microstructure of rain has been studied with observations using a vertical looking Micro Rain Radar (MRR) at Ahmedabad (23.06°N, 72.62°E), a tropical location in the Indian region. The rain height, derived from the bright band signature of melting layer of radar reflectivity profile, is found to be variable between the heights 4600 m and 5200 m. The change in the nature of rain, classified on the basis of radar reflectivity, is also observed through the MRR. It has been found that there are three types of rain, namely, convective, mixed and stratiform rain, prevailing with different vertical rain microstructures, such as, Drop Size Distribution (DSD), mean drop size, rain rate, liquid water content and average fall speed of the drops at different heights. It is observed that the vertical DSD profile is more inhomogeneous for mixed and stratiform type rain than for convective type rain. It is also found that the large number of drops of size <0.5 mm is present in convective rain whereas in stratiform rain, drops concentration is appreciable up to 1 mm. A comparison of measurements taken by ground based Disdrometer and that from the 200 m level obtained from MRR shows good agreement for rain rate and DSD at smaller rain rate values. The results may be useful for understanding rain structures over this region.     

Development of one-minute rain-rate and rain-attenuation contour maps for satellite propagation system planning in a subtropical country: South Africa

Millimeter and microwave system design at higher frequencies require as input a 1-min rain-rate cumulative distribution function for estimating the level of degradation that can be encountered at such frequency bands. Owing to the lack of 1-min rain-rate data in South Africa and the availability of 5-min and hourly rainfall data, we have used rain-rate conversion models and the refined Moupfouma model to convert the available data into 1-min rain-rate statistics. The attenuation caused by these rain rates was predicted using the International Telecommunication Union (ITU) recommendations model. The Kriging interpolation method was used to draw contour maps over different percentages of time for spatial interpolation of rain-rate values into a regular grid in order to obtain a highly consistent and predictable inter-gauge rain-rate variation over South Africa. The present results will be useful for system designers of modern broadband wireless access (BWA) and high-density cell-based Ku/Ka, Q/V band satellite systems, over the desired area of coverage in order to determine the appropriate effective isotropically radiated power (EIRP) and receiver characteristics of this region.     

Analysis of precipitable water vapor from GPS measurements in Chengdu region: Distribution and evolution characteristics in autumn

The rainfall process of Chengdu region in autumn has obvious regional features. Especially, the night-time rain rate of this region in this season is very high in China. Studying the spatial distribution and temporal variation of regional atmospheric precipitable water vapor (PWV) is important for our understanding of water vapor related processes, such as rainfall, evaporation, convective activity, among others in this area. Since GPS detection technology has the unique characteristics, such as all-weather, high accuracy, high spatial and temporal resolution as well as low cost, tracking and monitoring techniques on water vapor has achieved rapid developments in recent years. With GPS–PWV data at 30-min interval gathered from six GPS observational stations in Chengdu region in two autumns (September 2007–December 2007 and September 2008–December 2008), it is revealed that negative correlations exist between seasonally averaged value of GPS–PWV as well as its variation amplitude and local terrain altitude. The variation of PWV in the upper atmosphere of this region results from the water vapor variation from surface to 850 hPa. With the help of Fast Fourier Transform (FFT), it is found that the autumn PWV in Chengdu region has a multi-scale feature, which includes a seasonal cycle, 22.5 days period (quasi-tri-weekly oscillation). The variation of the GPS–PWV is related to periodical change in the transmitting of the water vapor caused by zonal and meridional wind strengths’ change and to the East Asian monsoon system. According to seasonal variation characteristics, we concluded that the middle October is the critical turning point in PWV content. On a shorter time scale, the relationship between autumn PWV and ground meteorological elements was obtained using the composite analysis approach.     

GNSS极化测量降雨正演模拟与实验对比

研究证明,全球导航卫星系统（GNSS）极化无线电掩星（PRO）技术可以用于探测降雨。利用GPM DPR降雨率数据与PAZ卫星极化相移观测数据匹配,筛选出代表性降雨事件。通过选用TB等7种雨滴形状和MP等5种雨滴谱模型,采用T矩阵法对各事件进行正演,并分析PAZ极化相移的线性校正值、天线相位校正值与正演模拟值之间的关系。对比分析得出线性校正值、相位校正值与模拟值的相关系数分别为0.9994和0.9933,均方根差分别为0.3429和1.2765。模拟值与实测值之间高度相关,且更接近线性校正值。进一步的研究表明,模拟降雨率在1 mm·h–1以下的事件时,雨滴谱采用MP或JD分布,雨滴形状采用SC或PB的模拟精度更高;降雨率在1 mm·h–1以上的事件,雨滴谱采用MP或 SS分布,雨滴形状采用TB的模拟结果最优。      

Fade duration statistics for Ku-band satellite links

Fade duration database was built to enhance the study of propagation characterises in the Equatorial region. The data was measured via a beacon receiver Ku-band whereby the antenna was directed to a SUPERBIRD-C2 satellite at 12.255 GHz. The performance of the measured data has been compared with ITU-R model, Kormanyos et al. and Paulson–Gibbins. The results show that the Paulson–Gibbins fits well with measured data with a low RMS error of 0.2 dB. The number of statistics available for the equatorial is small and the periods of measurement are short compared to those for temperate regions.     

基于数值预报模式的Ka频段卫星系统降雨衰减短时预测

为精确地实现Ka频段星地链路降雨衰减的短时趋势和强度预测,提出了一种基于数值天气预报的降雨衰减短时预测方法,设计了降雨衰减预测流程,并应用上述方法对某地区发生的降雨过程进行研究,比较分析了降雨衰减的实测值和基于数值预报的降雨衰减预测值。试验结果表明,基于数值预报的降雨衰减和实测值变化趋势符合较好,误差较小,在20GHz频段平均误差约为1.31dB,在30GHz频段平均误差约为2.39dB,在20GHz频段平均误差约为1.31dB,在30GHz频段平均误差约为2.39dB。该方法可实现Ka频段卫星通信系统的短时降雨趋势预测。     

Ground validation of space-borne satellite rainfall products in Malaysia

Precipitation studies globally is not only important to hydrological cycling, but also very crucial to satellite and terrestrial communication system designs. This work presents the validation of ground based rainfall measurement with TRMM products and GPCC data. The result shows that an error bias of ±15 % exists between the ground and satellite rainfall measurements. The study also shows that the TRMM 3B43 V6 is in good agreement with the rain gauge rainfall measurement with highest and lowest correlation coefficient of 0.9721 and 0.7791 respectively. Therefore, TRMM 3B43 V6 is recommended for use in lieu of the ground measurement in Malaysia and its environs, most importantly for sea and remote areas where rain gauge cannot cover.     

Higher order ionospheric error correction in BDS precise orbit determination

The ionospheric error affects the accuracy of the Global Navigation Satellite Systems observation and precise orbit determination. Usually, only the first order ionospheric error is considered, which can be eliminated by the ionospheric-free linear combination observation. But the remaining higher order ionospheric error will affect the accuracy of observations and their applications. In this paper, the influence of the higher order ionospheric error have been studied by using the International Geomagnetic Reference Field 13 and the Global Ionosphere Maps model produced by the Center for Orbit Determination in Europe. Focus on ionospheric error, the experiment of paper at doy 302 in 2019, which show that the second order ionospheric error impacting BeiDou Navigation Satellite System (BDS) B1I and B3I observation is 6.3569 mm and 11.8484 mm, respectively. Whereas, the third order ionospheric error impacting BDS B1I and B3I observation is 0.1734 mm and 0.3977 mm, respectively. Due to the current measurement accuracy of BDS carrier-phase observation can reach 2 mm, the influence of high order ionospheric error on observation should be considered. For BDS precise orbit determination, the orbit overlapping results are indicated that its orbit accuracy can be improved approximately 5 mm with the higher order ionospheric error correction, which is also in agreement with the results of Satellite Laser Ranging in this work.     

Variation of rain drop size distribution with rain rate at a few coastal and high altitude stations in southern peninsular India

Rain drop size distribution (DSD) was measured at four places in Southern India {Thiruvananthapuram, Kochi, Munnar and Sriharikota (SHAR)} using a Joss–Waldvogel (JW) impact type disdrometer. The data for each minute were corrected for dead time errors and rain rate was computed from the corrected data. The data for a whole month were then sorted according to rain rate (R) into several classes ranging from 0.1 to >100 mm/h. The average DSD in each class was computed, and the lognormal distribution function was fitted to the average. In all the cases, the function fitted the data very well. The fit parameters were found to have dependence on rain rate. The total number of drops (N_T), the geometric mean diameter (D_g) and the standard geometric deviation (σ) were also computed from the fit parameters. The standard geometric deviation (σ) was found to be more or less constant with rain rate at all the sites and in all months. The other two parameters (N_T and D_g) were found to vary exponentially with rain rate except in Munnar, a high altitude station. At Thiruvananthapuram, in most of the months, N_T increased exponentially with rain rate up to some value of R, which was different in different months, and then remained more or less constant or decrease slightly. In all cases, the variation of N_T and D_g was such that N_TD_g³ increased linearly with rain rate.     

High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/?0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.     

Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan

Because of global warming, global sea levels have risen, the frequency of drought in Taiwan is much more frequent in winter and spring, and rainfall tends to concentrate in summer. The probability of disaster-type weather has also increased significantly. Estimating precipitable water vapor (PWV) through GPS signals, related studies and analyses of weather conditions, and the effective use of meteorological forecasts have been valued by many meteorological research organizations and officials. In this study, PWV data from 2006 to 2017 and rainfall data were used for long-term harmonic analysis. PWV data calculated by ECMWF (ECMWF-PWV) and PWV data calculated by GPS (GPS-PWV) were subjected to regression analysis to verify the reliability of the GPS-PWV data. The research results show that GPS-PWV and ECMWF-PWV have extremely high correlations; however, the climatic characteristics of some regions and the high spatial resolution of GPS-PWV are able to accurately calculate the high topographic relief of small areas. It is judged that the GPS-PWV is more accurate than the ECMWF-PWV. It is worth noting that the PWV trend of the regions during the 6-year-before period has not changed very much, but the rainfall trend has changed obviously. Except for the eastern region, most of the regions show a decreasing trend year by year. More long-term observations are still needed to prove whether this phenomenon relates to global warming. Long-term rainfall analysis showed that the topography blocked water vapor to the western, southern, and mountainous regions, making them distinctly wet or dry. The harmonic curve showed great consistency with the peaks of PWV and rainfall. However, in the northern and eastern parts of the windward side, the time when maximum rainfall occurred each year may be one month later than the time when the maximum PWV value occurred each year. The reason for this difference is likely to be a decrease in the number of autumn typhoons, resulting in a nearly one-month difference in PWV peaks and rainfall peaks. Finally, we analyzed the linear trend of GPS-PWV and temperature for all regions in Taiwan, and found that annual increasing rate of GPS-PWV and temperature of all regions are within 0.4–0.5 mm/year and 0.04–0.11 C°/year, respectively.     

Comparative analysis of four different single-frequency PPP models on positioning performance and atmosphere delay retrieval

Single-frequency precise point positioning (SF-PPP) has attracted increasing attention due to its high precision and cost effectiveness. With various strategies to handle the dominant error, i.e., ionosphere delay, the ionosphere-float (IF), ionosphere-free-half (IFH), ionosphere-corrected (IC), and ionosphere-weighted (IW) SF-PPP models are certain to possess different characteristics and performance levels. This study is dedicated to assessing and comparing the four models from model characteristics, positioning performance, and atmosphere delay retrieval. The model comparison shows that IC and IW models are full-rank while IF and IFH models have a rank deficiency of size one that will result in biased estimations, which means the better solvability of IC and IW models. The experiments are carried out based on the 7-day Global Positioning System (GPS) observations collected at 57 global Multi-GNSS Experiment (MGEX) stations and Global Ionosphere Map (GIM) products. The results indicate that the IW model can accelerate SF-PPP convergence and achieve higher positioning accuracy compared to the other three SF-PPP models, especially in kinematic mode. With convergence criteria of 0.25 m in horizontal and 0.5 m in vertical, the east/north/up convergence times of IW model are 0.5/15.0/25.0 min and 0.5/16.0/36.5 min for static and kinematic modes, respectively. The IW model is able to achieve an instantaneous positioning accuracy of 0.28/0.35/0.75 m. In addition, a real kinematic test also demonstrates the best positioning solutions of IW model. Regarding troposphere delay retrieval, the IF, IFH, and IW models obtain a comparable daily accuracy of 3.0 cm on average, while the IC model achieves the worst accuracy of 8.0 cm. For precise ionosphere delay estimation, IW model only needs an average initialization time of 34.3 min, but a longer initialization time of 51.6 min is required for IF model. The daily precision of ionosphere delay estimation for IW model can reach up to 10.8 cm. At the present accuracy of GIM products, it is suggested that the IW model should be adopted for SF-PPP first due to its superior performance in positioning and atmosphere delay retrieval.