Reliability of satellite-based precipitation products in capturing extreme precipitation indices over Iran

The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices.     

Comprehensive evaluation of Satellite-Based and reanalysis precipitation products over the Mediterranean region in Turkey

Precipitation is an important component of the hydrological and energy cycles, as well as a key input parameter for many applications in the fields of hydrology, climatology, meteorology, and weather forecasting research. As a result, estimating precipitation accurately is critical. The purpose of this research is to conduct a comprehensive and comparative evaluation of grid-based precipitation products over Turkey's Mediterranean region from 2017 to 2021 at monthly and grid scales, using data from 193 ground-based meteorological stations as a reference. PERCIANN CCS, PDIR-Now, GSMaP MVK, PERSIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 are the eight grid-based precipitation products. Several prospective were used to evaluate the products, including magnitude agreement with gauge stations for the entire region and the six hydrological sub-basins included in the region, performance in capturing various intensity categories, and elevation dependency. According to the evaluation results, PERCIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 performed well in all evaluation aspects, whereas PERCIANN CCS, PDIR-Now, and GSMaP MVK performed poorly in all metrics. The majority of the products underestimated heavy rainfall events, while all products performed better at low and moderate precipitation events. As a result, the products performed better in the summer and spring months (March to October) than in the winter months (December to February). Furthermore, the results showed that the performance of the majority of the products degraded for elevations greater than 1000 m. The evaluation suggests that PERSIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 can be used as good precipitation data sources and as a complement to ground-based meteorological stations in Turkey's Mediterranean region.     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).     

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.     

Improved rainfall estimation over the Indian region using satellite infrared technique

The GOES Precipitation Index (GPI) technique (Arkin, 1979) for rainfall estimation has been in operation for the last three decades. However, its applications are limited to the larger temporal and spatial scales. The present study focuses on the augmentation on GPI technique by incorporating a moisture factor for the environmental correction developed by Vicente et al. (1998). It consists of two steps; in the first step the GPI technique is applied to the Kalpana-IR data for rainfall estimation over the Indian land and oceanic region and in the second step an environmental moisture correction factor is applied to the GPI-based rainfall to estimate the final rainfall. Detailed validation with rain gauges and comparison with Tropical Rainfall Measuring Mission (TRMM) merged data product (3B42) are performed and it is found that the present technique is able to estimate the rainfall with better accuracy than the GPI technique over higher temporal and spatial domains for many operational applications in and around the Indian regions using Indian geostationary satellite data. Further comparison with the Doppler Weather Radar shows that the present technique is able to retrieve the rainfall with reasonably good accuracy.     

Accurate estimation of relative atmospheric density error on the example of uncertain geomagnetic activity information

Errors in neutral atmospheric density are the dominant contributor to unrealistic orbital state-vector covariances in low Earth orbits (LEO). Density uncertainty is caused by model-uncertainty at spatial scales below and within the model resolution, as well as input-uncertainty of the environmental parameters supplied to the semi-empirical atmospheric model.The paper at hand provides multiple contributions. First, analytic equations are derived to estimate the relative density error due to an input parameter uncertainty in any of the environmental parameters supplied to the model. Second, it is shown on the example of uncertain geomagnetic activity information, how to compute the required inputs to facilitate the accurate estimation of the relative density error.A clamped cubic splining approach for the conversion from geomagnetic amplitude (ap) to the kp index is postulated to perform this uncertainty propagation, as other algorithms were found unsuitable for this task. Results of numerical simulations with three popular semi-empirical models are provided to validate the set of derived equations. It is found that geomagnetic input uncertainty is especially important to consider in case of low global geomagnetic activity. The findings seamlessly integrate with prior work by the authors to perform density-uncertainty considering orbit estimation.     

乏信息空间机械臂随机振动信号的灰自助评估

经典的统计学方法无法解决乏信息数据的评估问题。结合自助法和灰色系统理论,提出一种实现乏信息空间机械臂随机振动数据估计的灰自助方法。运用自助法对乏信息振动功率谱密度进行自助再抽样得到大量样本数据;利用灰色系统理论和最大熵理论建立灰自助模型,构建振动功率谱密度在不同频率点的灰自助分布。利用灰自助方法得到随机振动功率谱密度的真值估计和区间估计。提出了可靠度偏差和区间准确度2个指标对区间估计进行评价。灰自助方法与灰色方法和自助法的对比与测量实例表明,真值估计平均相对误差小于5%,在不同置信度水平下区间估计的准确度高于97%。      

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。      

Advanced ensemble modeling method for space object state prediction accounting for uncertainty in atmospheric density

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed “HASDM-ML,” “CHAMP-ML,” and “MSIS-UQ” machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density values. We develop several Monte Carlo methods, each capturing a different spatiotemporal density correlation, to study the effects of density uncertainty on orbit uncertainty propagation. However, Monte Carlo analysis is computationally expensive, so a faster method based on the Kalman filtering technique for orbit uncertainty propagation is also explored. It is difficult to translate the uncertainty in atmospheric density to the uncertainty in orbital states under a standard extended Kalman filter or unscented Kalman filter framework. This work uses the so-called “consider covariance sigma point (CCSP)” filter that can account for the density uncertainties during orbit propagation. As a test-bed for validation purposes, a comparison between CCSP and Monte Carlo methods of orbit uncertainty propagation is carried out. Finally, using the HASDM-ML, CHAMP-ML, and MSIS-UQ density models, we propose an ensemble approach for orbit uncertainty quantification for four different space weather conditions.     

数字多用表示值误差测量不确定度自动评定的设计

针对数字多用表型号多、校准项目多,校准后应给出整个测量范围内的测量结果不确定度,采用手动进行不确定度评定费时费力、容易出错、工作效率低等问题,在VEE Pro软件开发平台上,设计开发出数字多用表示值误差测量结果不确定度自动评定系统。该系统包括带接口数字多用表不确定度评定和不带接口数字多用表不确定度评定两个模块,可自动进行不确定度评定及存档。同时结合实例,给出了直流电压示值误差测量不确定度评定结果。使用结果表明：该系统操作简便,提高了工作效率及准确度,减小了人为误差。     

压力传感器动态特性参数不确定度评定

压力传感器动态特性参数的不确定度是表征其动态测量性能的重要指标。提出了一种压力传感器动态特性参数的不确定度评定方法。首先,使用激波管动态校准系统产生阶跃压力信号激励压力传感器,得到传感器的输出信号;其次,采用基于经验模态分解（EMD）的传感器输出信号预处理方法,减小动态校准过程中噪声的影响;然后,根据传感器的输入输出信号,采用自适应最小二乘法建立压力传感器的数学模型,进而得到其时频域动态特性参数;最后,针对重复校准实验得到的动态特性参数序列的小样本特点,采用自助法计算参数的扩展不确定度和相对不确定度。采用激波管系统对压力传感器进行多次重复动态校准实验,计算时频域动态特性参数的不确定度,并与现有方法进行对比。实验结果表明:本文方法可以弥补贝塞尔法在处理小样本量数据中的不足,且与蒙特卡罗法的不确定度评定结果相对误差小于10%,说明本文方法可以有效地评定压力传感器动态特性参数的不确定度。分析时频域动态特性参数的相对不确定度得到传感器的工作频带和超调量受噪声的影响较大,为动态校准实验条件的改善提供了重要依据。      

Comparison of observed ionospheric vertical TEC over the sea in Indian region with IRI-2016 model

The vertical ionospheric TEC values obtained from GAGAN grid based ionospheric delay correction values over the sea in the Indian equatorial region have been compared with the corresponding values derived from the International Reference Ionosphere model, IRI-2016. The objective of this work is to study the deviation of the vertical TEC derived from the IRI model from ground truths over the sea for different conditions. This will serve the basic intention of assessing the candidature of the IRI model as an alternative ionospheric correction model in navigation receivers in terms of accuracy. We have chosen different solar activity periods, seasons, geomagnetic conditions, locations etc. for our comparison and analysis. The TEC values by the IRI-2016 were compared with the actual measured values for the given conditions and errors were obtained. The measured vertical TEC values at the ionospheric grid points were derived from the GAGAN broadcast ionospheric delay data and used as reference. The IRI model with standard internal functions was used in estimating the TEC at the same ionospheric grid points. The errors in the model derived values are statistically analysed. Broadly, the results show that, for the Indian sector over the sea, the IRI model performs better on quiet days in off equatorial regions, particularly in the northern region. The overall performance degrades for other conditions with the model generally underestimating the true TEC values and most severely in the equatorial region. The performance is worst in this region for the disturbed days of the equinoctial period. The comparison study is also done with the TEC data measured directly by dual frequency GPS receivers. The results were found to be in general agreement with those obtained by comparing the model with GAGAN broadcast data as reference. This study will be useful in considering the IRI-2016 model for real time estimates of TEC as an alternative to the current parametric model in a satellite navigation receiver in absence of other options.     

POBI interpolation algorithm for CYGNSS near real time flood detection research: A case study of extreme precipitation events in Henan,China in 2021

In late 2016, NASA launched the first constellation of the global navigation satellite system reflectometry (GNSS-R) small satellites called the Cyclone Global Navigation Satellite System (CYGNSS). The stable data quality and continuous free availability of CYGNSS scientific data provided a new method for flood monitoring. However, owing to the pseudorandom distribution of CYGNSS data, researchers must always choose between high temporal resolution and high spatial resolution during the performance of flood monitoring based on CYGNSS data. For floods caused by extreme precipitation with sudden and short durations, the current flood mapping based on CYGNSS data cannot be updated in near real time. However, the near real time update of the flood distribution range is meaningful for postdisaster emergency response and rapid rescue. This study aimed to address this problem using a newly proposed spatial interpolation method based on previously observed behaviour (POBI). First, a method for calculating the surface reflectivity of the CYGNSS was introduced, followed by the principle of the POBI spatial interpolation method. The applicability of the POBI method in Henan Province, China, was then analysed, and by using the flood in Henan Province, China, in July 2021 as an example, the feasibility of CYGNSS near real time flood mapping based on the POBI method was evaluated. Based on the results, near real time and 3 km flood distribution monitoring results can be obtained using the proposed new method. The results were evaluated using MODIS (Moderate Resolution Imaging Spectroradiometer) images and compared with the observations of SMAP (Soil Moisture Active Passive) and GPM (Global Precipitation Measurement) in the same period. The results show that the flooded areas obtained by CYGNSS correspond to the inundated areas in MODIS images and are also in high agreement with the SMAP. In addition, CYGNSS allows for finer mapping and quantification of inundation areas and flood duration. Moreover, we also discussed the potential of CYGNSS to detect floods in shorter periods of time (a few hours) and did a preliminary evaluation using precipitation data from meteorological stations. The results are also highly consistent.     

两种J_2摄动模型下卫星编队相对位置误差分析

为了研究一个轨道周期内卫星和卫星编队的运动规律,在J2摄动理论基础之上,采用摄动加速度分析方法,给出了J2瞬时摄动模型。以近地太阳同步轨道卫星和双星编队为例,与只考虑J2一阶长期项的平均摄动模型比较,仿真分析结果表明,对卫星而言,一个轨道周期内,卫星半长轴相对平均半长轴漂移达到18km,偏心率相对平均偏心率漂移达到10-3量级,轨道倾角相对平均轨道倾角漂移达到0.01°,即由于J2瞬时摄动的影响,卫星运动发生了摄动;对双星编队而言,一个轨道周期内,两星相对位置的径向误差达到5km,沿迹向误差达到19km,法向误差相对较小,在10-2量级上,相对距离的误差达到了19km,随着时间的推移,误差会越来越大。     

脉冲激光测距误差标定及不确定度分析CSCD

以脉冲激光测距机的测距误差标定为背景,通过对测距误差解析表达式的推导,给出了系统各不确定度分量对测距误差的影响,计算了信号延迟时间、探测器和激光二极管响应时间、晶振频率、大气折射率等不确定度分量。通过对某已标定激光测距机实验验证表明:该装置在500m^20 000m测距范围内,测量重复性引入的相对标准不确定度最大值为0.67m,满足该类激光测距机测量不确定度5m(k=2)的校准测试需求。因此,利用该解析表达式可以实现对脉冲激光测距误差的有效评估,这对于脉冲激光测距测试系统、脉冲激光测距机的设计具有重要的指导意义。     

动态测量不确定度A类评定

综述动态测量误差与不确定度评定的基本特点、一般评定指标与评定方法。着重概述对动态测量数据作统计分析的不确定度Ａ类评定方法。列举各种常用动态测量数据处理方案及其基本假定条件和测量误差分离原理及测量不确定度Ａ类评定的基本算法。     

相对测量不确定度在微波功率计量中的应用CSCD

本文探讨了相对测量不确定度在微波功率计量中的相关应用,通过微波功率计量中的两个实例,分析了相对测量不确定度的数学模型及评定过程,规范了其表述规则,全面概括了实际应用中的注意事项。     

Precise line-of-sight modelling for angles-only relative navigation

This work presents a precise analytical model to reconstruct the line-of-sight vector to a target satellite over time, as required by angles-only relative navigation systems for application to rendezvous missions. The model includes the effects of the geopotential, featuring: the analytical propagation in the mean relative orbital elements (up to second-order expansion), the analytical two-way osculating/mean orbital elements’ conversion (second-order in $J_2$ and up to a given degree and order of the geopotential), and a second-order mapping from the perturbed osculating elements’ set to the local orbital frame. Performances are assessed against the line-of-sight reconstructed out of the precise GPS-based positioning products of the PRISMA mission. The line-of-sight modelled over a far-range one day long scenario can be fitted against the true one presenting residuals of the order of ten arc-seconds, which is below the typical sensor noise at far-range.