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.     

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.     

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.     

Performance evaluation of some rain rate conversion models for microwave propagation studies

An important characteristic of rainfall levels at a particular place is the statistical distribution of rainfall rate. In this paper, 5-min integration time rainfall data for the Northcentral region of Nigeria was obtained from the Tropospheric Data Acquisition Network (TRODAN), Anyigba, Nigeria. Also, 1-min integration time rainfall was measured at Minna, Nigeria. In order to obtain the optimal rain rate model suitable for this region, two globally recognised rain rate models were critically evaluated and compared with the 1-min measurements. These are the ITU-R P.837-7 and Lavergnat-Gole (L-G) models. The results obtained showed that the ITU-R P.837-7 and L-G models respectively underestimated the measured rain rate by 7.3 mm/h and 9 mm/h at time percentage exceedance of 0.1%, while they underestimated the measured rain rate by 23.4 mm/h and 13 mm/h respectively at 0.01%. At 0.001%, the measured rain rate was overestimated by the ITU-R P.837-7 and L-G models by 27.4 mm/h and 3 mm/h respectively. Further performance evaluation of the predefined models was carried out using different error metrics such as sum of absolute error (SAE), mean absolute error (MAE), root mean square error (RMSE), standard deviation (STDEV) and Spearman’s rank correlation. The results obtained adjudged the Lavergnat-Gole model as the best rain rate prediction model for this region.     

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.     

Using S-band dual polarized radar for convective/stratiform rain indexing and the correspondence with AMSR-E GSFC profiling algorithm

The separation of rain types in convective and stratiform regimes has long been a goal in microwave remote sensing of precipitation research. In this essence, a dual polarized radar based indexing scheme that provides information on convective and stratiform (C/S) rain regimes has been presented in correspondence with advanced microwave scanning radiometer – earth observing system (AMSR-E) GSFC profiling algorithm estimate of convective rain percentage. The dual polarized radar based C/S indexing scheme first retrieves the normalized gamma drop size distribution parameters, median volume drop diameter (D₀) and concentration parameter (N_w), from dual polarized radar measurements Z_H and Z_DR, representing reflectivity and differential reflectivity respectively, by means of the genetic programming approach. Next, the C/S rain index is calculated based on the formulation of an empirical relation in N_w–D₀ domain. The scheme has been inspected and applied on measurements from the S-band Chilbolton dual polarized radar. A considerable number of “coincident” cases from the radar and the AMSR-E observations are investigated. It has been revealed that the dual polarized radar based C/S rain indexing is in a similar pattern with the AMSR-E GSFC profiling algorithm estimate of convective rain percentage. Generally, as C/S rain index value increases, which signifies a stratiform to convective trend, the AMSR-E convective rain percentage also increases.     

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.     

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.     

基于CO2测量数据的大气辐射传输模型LBLRTM优化

根据TIMED/SABER 2002—2018年的CO₂观测数据,分析CO₂浓度的变化特征.依据变化特征给出了CO₂浓度随时间、高度、纬度变化的月平均拟合公式,利用非线性最小二乘拟合法,对不同高度和不同纬度的CO₂浓度数据分别进行拟合,生成相应的拟合参数.然后,将所有拟合参数汇总并生成拟合参数文件,结合拟合公式构建全球CO₂浓度经验计算模块,并将该模块应用到大气辐射传输模型LBLRTM中,对该模型进行优化.将优化前与优化后的LBLRTM模型模拟结果分别与TIMED/SABER观测数据进行比较发现,优化前的LBLRTM模型模拟结果与观测值的均方根误差为15.4%,而优化后的LBLRTM模型模拟结果与观测值的均方根误差由15.4%下降至8.91%.结果表明该优化方法可以提高LBLRTM模型在红外波段的辐射模拟精度.      

Longitudinal distribution of thermospheric densities and ionization rates in the upper atmosphere of Mars at mid-latitude using MGS ACC data

The Accelerometer Experiment (ACC) onboard Mars Global Surveyor (MGS) measured 1600 density profiles in the upper atmosphere of Mars during aerobraking. These measurements reveal large-scale and small-scale structure in the thermosphere of Mars. Here, the measurements of mass density for 115 orbits (#P0670–P0789) from November 1 to 30, 1998, under spring equinox and medium solar activity conditions (average F_10.7 ∼ 137) during phase 2 of the aerobraking in the thermosphere of Mars at different altitudes and longitudes are presented for northern mid-latitude (17–42°N) in the dayside atmosphere using ACC onboard MGS. From these mass densities, the neutral densities of different gases are derived from their mixing ratios. Using these neutral densities, the longitudinal distribution of photoionization rates and photoelectron impact ionization rates are calculated at wavelength range 1–102.57 nm due to EUV and soft X-ray radiation under photochemical controlled region using Analytical Yield Spectrum approach (AYS). These conditions are appropriate for MGS Phase 2 aerobraking period from which the accelerometer data is used. Under the photochemical equilibrium condition, the electron density near the peak varies as the square root of the total peak ionization rate. Using this fact, an attempt is being made to estimate the mean primary and secondary peak electron density by averaging the longitudinal variations of total peak ionization rates in the northern mid-latitude (17–42°N) ionosphere of Mars, as there is no radio science measurement at this latitude region by MGS.     

Variations in polar rain flux according to IMF geometries observed by STSAT-1

We examined polar rain flux observed by STSAT-1 in the northern polar cap and compared it with solar wind parameters. We found that the differential energy spectrum of polar rain was similar to that of the solar wind for the energy range 100 eV – 1 keV, although we cannot rule out the possibility of a small amount of acceleration. On the other hand, the low-energy component of the solar wind showed no correlation and, naturally, the solar wind density had only a weak correlation with the polar rain flux. Polar rain flux in the northern hemisphere is most significant for the condition of the interplanetary magnetic field components Bz < 0, Bx < 0, and By > 0, and in this case it correlated well with the magnitude of By and Bz. For other interplanetary magnetic field conditions, the correlation was insignificant. The results are consistent with those reported previously.     

基于Bayesian-MCMC估计的隐身飞机RCS模型优化

对隐身飞机的雷达散射截面(RCS)统计建模时,传统方法通过直接计算RCS样本的统计特征估计模型参数,可能会产生较大的拟合误差。本文提出采用贝叶斯-蒙特卡罗(Bayesian-MCMC )方法提高起伏模型的参数估计精度,从而减小模型的拟合误差。首先将卡方分布模型和对数正态分布模型进行贝叶斯推导,得到其特征参数的后验估计表达式。然后采用MCMC算法构造后验分布的马尔可夫链,从而计算特征参数的估计值。最后通过比较2种方法的拟合曲线及其误差可知,本文方法适用于2种起伏模型,模型参数的估计误差比收敛误差门限值低1~2个数量级,2种分布模型的拟合精度均提高50%以上。      

利用卫星两行轨道根数反演热层密度

两行轨道根数（TLEs）是基于一般摄动理论产生的用于预报地球轨道飞行器位置和速度的一组轨道参数,通过求解大气阻力微分方程,可反演出热层大气密度. 本文选取近圆轨道CHAMP卫星和椭圆轨道Explorer8卫星,以两行轨道根数数据为基础,计算反弹道系数,并根据不同轨道特征采用两种不同反演方法对热层大气密度进行研究. 结果表明,这两种方法反演得到的大气密度与实测值均符合较好,其中CHAMP卫星的反演结果和经验模式值相对于实测值的误差分别为7.94%和13.94%,Explorer8卫星的误差分别为9.04%和14.32%. 相比模式值,利用两行轨道根数数据反演的热层大气密度更接近于实测值,说明该方法可以作为获取大量可靠大气密度数据的一种有效途径.      

涌浪及降雨影响下的GNSS海面反射信号建模

全球导航卫星系统（GNSS）海面反射信号的模拟仿真采用的海浪谱多为风驱模型,而忽略了真实复杂环境中涌浪、降雨的影响。为此,提出了一种涌浪、降雨影响下的GNSS海面反射信号模型。首先,对Elfouhaily海浪谱、涌浪谱、降雨谱分别进行仿真,从海浪谱的角度分析涌浪、降雨对GNSS反射信号的影响。然后,设计了引入涌浪、降雨影响因子后的GNSS反射信号建模的方法,并建立噪声模型。最后,对星载场景下仿真得到的二维时延-多普勒相关功率分布图像（DDM）、时延相关功率波形（DW）进行分析,并与英国技术演示卫星（UK TDS-1）实测数据的处理结果进行了对比验证。结果表明:涌浪主要形成对GNSS反射信号影响较大的大尺度粗糙海面,而降雨对GNSS反射信号影响较小;仿真的DDM与实测数据结果的波形有很好的一致性,DW对比的相关系数达到0.92,优于未修正模型的对比结果,模拟的反射信号更为真实,证明了提出的GNSS反射信号建模方法的可行性、有效性。对真实复杂环境下的GNSS反射信号建模及GNSS反射信号星载探测应用研究具有一定的参考意义和实用价值。      

Coronal models of late-type stars observed with EXOSAT

Differential emission measure distributions for transition region and corona able to reproduce X-ray and UV emission are investigated. The EXOSAT, EINSTEIN and IUE data for the solar type star K Cet are analyzed. A multitemperature differential emission measure distribution gives a satisfactory fit of the data.     

Dust acoustic dressed solitons in a four component dusty plasma with nonthermal electron

Nonlinear dust acoustic dressed soliton are studied in a four component dusty plasma. Nonthermal distributions for electrons are considered. The Korteweg–de Vries (KdV) equation is derived by using reductive perturbation technique. A higher order inhomogeneous differential equation is obtained for the higher order correction. The expression for dressed soliton is obtained by the renormalization method. The expressions for higher order correction are determined using a series solution technique.     

The possible applications of Meteosat for monitoring the rain season in the Sahel zone: The case of Senegal

The use of geostationary meteorological satellites for monitoring climate is relatively well known. However, the application of satellite data for agronomical purposes is still far form being operational. Recent work shows the possibility of establishing statistical models adapted to each region which can predict rain to within a time interval of 12 to 24 hours, based on the analysis of cloud cover. In the same way, thermal IR images may be used, employing the quasi-linear relationship between surface temperature and real evapotranspiration E.T.R. This is the objective of the project on monitoring the hydric balance in Senegal, associating INRA-IRAT and LERTS in France, ISRA and Meteorologie Nationale in Senegal. The first results obtained by Assad et al from two Meteosat images taken during 1979 establishing an inverse relationship between surface temperature and rain, over the total area of Senegal, were maintained for 16 images taken during the 1984 and 1985 rain period. Their analyses show that it is possible : - To identify the most favorable sowing periods, - to diagnose periods of high climatic risk for crops, and to cartograph rain distribution from spatial variations in surface temperature.     

Regular features of the polar ionosphere characteristics from Digisonde measurements over Norilsk

Regular features of the polar ionosphere have been studied using its local empirical model of the electron density distribution in the bottomside ionosphere. The local empirical model was derived from the hand-scaled ionogram data recorded by DPS-4 Digisonde at Norilsk, Russia (69.4N, 88.1E; 60N GLAT, 166E GLON) for a 6-year period from December, 2002 to December, 2008. The paper describes the technique used to build the local empirical model and discusses its diurnal, seasonal, and solar activity specifications in comparison with the standard IRI-2007 climatological model for the same period of time, long-term observations from the European Incoherent Scatter UHF radar (1988–1999), and the high-latitude ionosondes data. Primary focus of the paper is behavior of the three F2 layer parameters: the F2 peak density (NmF2), the peak height (hmF2) and the bottomside thickness (B0). Special emphasis of the paper is the analysis of the winter anomaly manifestation at Norilsk and the peculiar diurnal–seasonal behavior of hmF2 under low solar activity, named as a “polar day effect”.     

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.     

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.