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.     

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.     

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.     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

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.     

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.     

Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites

In this study, integrated drought monitoring index (IDMI) was proposed as a tool to assess and monitor the spatio-temporal dynamics of agricultural drought during the northeast monsoon season for the period from 2000 to 2016 in Tamil Nadu state, south-eastern part of Indian peninsula. The IDMI is characterized as the principal component of precipitation condition index (PCI), soil moisture condition index (SMCI), temperature condition index (TCI), and vegetation condition index (VCI) derived from time-series satellite observations of climate hazards group infra-red precipitation with stations (CHIRPS), European space agency climate change initiative (ESA-CCI) and moderate resolution imaging spectroradiometer (MODIS). The study shows that in the year 2016, about 44.4 and 17.8% of Tamil Nadu state was under extreme and severe drought conditions, respectively. Sensitivity analysis of the study shows that PCI is the most influential parameter to IDMI, followed by VCI and TCI. The validation of IDMI with 3-month standardized precipitation index (SPI) by using Pearson correlation test shows a strong positive correlation between IDMI and 3-month SPI with correlation coefficient (r) value of 0.73 and 0.77 for the wet (2005) and dry year (2016), respectively. The study clearly demonstrates the potential of IDMI derived from time-series datasets of earth observation satellites as a tool in assessment and monitoring of spatio-temporal dynamics of agricultural drought. The proposed IDMI could be effectively used as a reliable tool to monitor agricultural drought and develop its mitigation strategies to minimise the adverse effects of drought on agriculture, water resources, and livelihoods of the people.     

Evaluation of GPM sampling error over major basins in Indian subcontinent using bootstrap technique

The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications.     

Variation of energetic particle precipitation with local magnetic time

The detailed study of the precipitation of magnetospheric particles into the atmosphere is complicated by the rather complex spatial configuration of the precipitation region and its variability with geomagnetic activity. In this paper we will introduce polar oval coordinates and apply them to POES observations of 30 keV to 2.5 MeV electrons and comparable protons to illustrate the dependence of particle precipitation on local time and geomagnetic activity. These coordinates also allow an easy separation of the spatial precipitation patterns of solar and magnetospheric particles. The results indicate that (a) the spatial precipitation pattern of energetic magnetospheric electrons basically follows the pattern of the field parallel Birkeland currents up to MeV energies and (b) at least in the mesosphere the influence of magnetospheric electrons is comparable to the one of solar electrons. Implications for modeling of atmospheric chemistry will be sketched.     

Solar particle precipitation into the polar atmosphere and their dependence on hemisphere and local time

The precipitation of solar energetic particles, protons as well as electrons, at high latitudes is commonly assumed to be homogeneous across both polar caps. Using Low-Earth Orbit POES (Polar Orbiting Environmental Satellites) we determine particle penetration ratios into the polar atmosphere for protons ranging from about 0.1 MeV to 500 MeV and for electrons spanning about one order of magnitude in energy with a maximum of 0.3 MeV. Based on power law fits for the POES spectrum we show, that for energies interesting for middle and lower atmosphere chemistry, particle flux over the poles is comparable in magnitude to flux at the geostationary orbit or at L1 in interplanetary space. The time period under study are the solar energetic particle (SEP) event series of October/November 2003 and January 2005.     

Investigation of PMSE dependence on high energy particle precipitation during their simultaneous occurrence

This paper is based on the observations of Polar Mesosphere Summer Echoes (PMSE) with the EISCAT VHF 224?MHz radar during the summer month 08–12 July 2013. The effect of high energy particle precipitation on PMSE intensity, particularly during their simultaneous occurrence for longer time interval (longer than or equal to 3-h) has been investigated. The correlation between the two phenomena has been computed using the Spearman rank and Pearson linear correlation coefficient. The variations in high energy particle precipitation reaching down to altitude of 91?km and PMSE intensity in the altitude range of 80–90?km are positively correlated. The electron density irregularity due to ionization caused by precipitating particles might be one of the possible reasons for this positive correlation. Moreover, some other background parameters i.e. K-indices (proxy of high energy particle precipitation) and electron fluxes during the simultaneous occurrence of the two phenomena also support one of the possible reasons given for explanation of the observed positive correlation. The X-rays and proton fluxes have no noticeable effect on PMSE echoes in this study.     

A prediction model of short-term ionospheric foF2 based on AdaBoost

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.     

Development of a regional rain retrieval algorithm for exclusive mesoscale convective systems over peninsular India

The present study emphasize the development of a region specific rain retrieval algorithm by taking into accounts the cloud features. Brightness temperatures (T_bs) from various TRMM Microwave Imager (TMI) channels are calibrated with near surface rain intensity as observed from the TRMM – Precipitation Radar. It shows that T_b–R relations during exclusive-Mesoscale Convective System (MCS) events have greater dynamical range compared to combined events of non-MCS and MCS. Increased dynamical range of T_b–R relations for exclusive-MCS events have led to the development of an Artificial Neural Network (ANN) based regional algorithm for rain intensity estimation. By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed. A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm.     

基于DTW的HJ-1B卫星地表温度产品质量检验

HJ-1B卫星热红外数据应用广泛,但其地表温度反演产品的质量检验工作尚不完善。以黑河流域为研究区,利用普适性单通道算法得到HJ 1B地表温度,基于7个地面站点（下垫面为荒漠、沙漠、植被、农作物、雪地和湿地）同步观测资料和MODIS地表温度产品（MOD11A1）,引入动态时间规整方法（DTW）对站点处HJ 1B地表温度进行验证。试验结果表明：HJ 1B地表温度反演产品与地面观测值的偏差值在沙漠和荒漠站点为1K以内,均方根误差在05K左右;对于植被和农作物站点的偏差在2K以内,均方根误差为1~2K;基于DTW的验证对时序不匹配的数据评价结果与现有指标表现一致。HJ-1B地表温度反演产品与地面站点的相对偏差均低于其与MODIS地表温度反演产品的相对偏差。     

Ni75Al4V21合金沉淀早期微观相场法模拟

采用三元体系微观相场动力学模型,对较低铝浓度的Ni₇₅Al₄V₂₁合金中γ′相和θ相沉淀早期进行计算研究,模拟了合金的原子图像演化过程,并计算了θ(Ni₃V)相和γ′(Ni₃Al)相的相内成分序参数分布和长程序参数分布.结果表明,θ相先析出,其沉淀机制为等成分有序化+失稳分解,等成分有序化产生非化学计量比的单相θ有序畴,并被相界分开,失稳分解为一特殊的分解类型,主要发生在相界处,形成化学计量比θ有序相;此失稳分解过程同时又是γ′相在θ相界处以非经典异相形核方式析出的过程,先形成非化学计量比γ′有序相,并逐渐长成为化学计量比γ′有序相,从而形成2种有序相共存的组织形态.     

Analysis of GNSS clock prediction performance with different interrupt intervals and application to real-time kinematic precise point positioning

Continuous and timely real-time satellite orbit and clock products are mandatory for real-time precise point positioning (RT-PPP). Real-time high-precision satellite orbit and clock products should be predicted within a short time in case of communication delay or connection breakdown in practical applications. For prediction, historical data describing the characteristics of the real-time orbit and clock can be used as the basis for performing the prediction. When historical data are scarce, it is difficult for many existing models to perform precise predictions. In this paper, a linear regression model is used to predict clock products. Seven-day GeoForschungsZentrum (GFZ) final clock products sampled at 30 s are used to analyze the characteristics of GNSS clocks. It is shown that the linear regression model can be used as the prediction model for the satellite clock products. In addition, the accuracy of the clock prediction for different satellites are analyzed using historical data with different periods (such as 2 and 10 epochs). Experimental results show that the accuracy of the clock with the linear regression prediction model using historical data with 10 epochs is 1.0 ns within 900 s. This is higher accuracy than that achieved using historical data of 2 epochs. Finally, the performance analysis for real-time kinematic precise point positioning (PPP) is provided using GFZ final clock prediction results and state space representation (SSR) clock prediction results when communication delay or connection breakdown occur. Experimental results show that the positioning accuracy without prediction is better than that with prediction in general, whether using the final clock product or the SSR clock product. For the final clock product, the positioning accuracy in the north (N), east (E), and up (U) directions is better than 10.0 cm with all visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 15.0 cm to 7.0 cm. For the SSR clock product, the positioning accuracy of N, E, and U directions is better than 12.0 cm with visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 12.0 cm to 9.0 cm.     

A study on possible solar and geomagnetic effects on the precipitation over northwestern Argentina

The precipitation over Tucuman (26.8°S; 65.2°W), which is representative of the Northwestern region of Argentina, is analyzed in search of an association with solar and geomagnetic activity, with the purpose of contributing to the controversial issue on the connection between climate variation and anthropogenic vs. natural forcing. Monthly time series of precipitation, sunspot number (Rz), and aa index were used for the period 1884–2010. A wavelet analysis was performed first which, due to the time series length, shows significant results only for periodicities lower than 32 years. Due to the transient character and non-constant phase of the results, any sustained wavelet coherence between precipitation and either sunspots or aa could be noticed. Moving averages and correlations were also assessed. The 11 and 22-year running mean of precipitation is positively correlated to Rz and aa when the whole period of analysis is considered. However, a shift in the long-term behavior of precipitation is noticed around 1940, which implies different correlation values with Rz and aa when the period before or after this year are considered. The solar cycle length is also considered for this statistical study and partly confirms the results obtained with Rz and aa. We propose plausible physical explanations based on geomagnetic activity and total solar irradiance effects over atmospheric circulation that could support the statistical result. A deeper analysis and broader geographical coverage is needed in order to detect a connection between precipitation and solar variability discernible from greenhouse gases effects. We emphasize the idea of the importance of recognizing and quantifying the different forcing acting on precipitation (or any other climate parameter), which sometimes can be barely evident from a solely statistical analysis.     

基于ICT分布的容迟网络接触模型

在容迟网络中,掌握节点之间的接触间隔时间(ICT)的特性,能够为网络性能分析、路由协议设计以及算法优化等研究提供理论指导和帮助,但目前的ICT模型往往缺乏普适性.通过对节点运动做出一般性假设,基于可靠性数学方法,给出了一个基于ICT分布的接触模型——IDCM.该模型证明了两个移动节点之间的ICT服从指数分布,且指数分布的参数仅与两个节点的历史接触次数和累积ICT有关.在随机方向(RD)移动模型、随机路点(RWP)移动模型、北京市出租车网络、口袋交换网4个数据集上进行了仿真验证,并与基于统计拟合参数的指数分布模型进行对比.仿真实验结果表明,IDCM能够准确反映节点对之间的接触间隔时间分布,且模型准确性优于基于统计拟合参数的指数分布模型.      

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.