Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty

The Global Precipitation Measurement mission planned jointly by the United States, Japanese, and European space agencies envisions providing global rainfall products from a constellation of passive microwave (PM) satellite sensors at time scales ranging from 3-6 h. In this paper, a sensitivity analysis was carried out to understand the implication of satellite PM rainfall retrieval and sampling errors on flood prediction uncertainty for medium-sized (/spl sim/100 km/sup 2/) watersheds. The 3-h rainfall sampling gave comparable flood prediction uncertainties with respect to the hourly sampling, typically used in runoff modeling, for a major flood event in Northern Italy. The runoff prediction error, though, was magnified up to a factor of 3 when rainfall estimates were derived from 6-h PM sampling intervals. The systematic and random error components in PM retrieval are shown to interact with PM sampling introducing added uncertainty in runoff simulation. The temporal correlation in the PM retrieval error was found to have a negligible effect in runoff prediction. It is shown that merging rain retrievals from hourly infrared (IR) and PM observations generally reduces flood prediction uncertainty. The error reduction varied between 50% (0%) and 80% (50%) for the 6-h (3-h) PM sampling scenarios, depending on the relative magnitudes of PM and IR retrieval errors. Findings from this paper are potentially useful for the design, planning, and application assessment of satellite remote sensing in flood and flash flood forecasting.     

Bayesian algorithm for microwave-based precipitation retrieval: description and application to TMI measurements over ocean

A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification.     

Ground-based multifrequency microwave radiometry for rainfallremote sensing

Inversion algorithms for ground-based microwave radiometric retrieval of surface rain-rate, integrated cloud parameters, and slant-path attenuation are proposed and tested. The estimation methods are trained by numerical simulations of a radiative transfer model applied to microphysically-consistent precipitating cloud structures, representative of stratiform and convective rainy clouds. The discrete-ordinate method is used to solve the radiative transfer equation for plane-parallel seven-layer structures, including liquid, melted, and ice spherical hydrometeors. Besides ordinary multiple regression, a variance-constrained regression algorithm is developed and applied to synthetic data in order to evaluate its robustness to noise and its potentiality. Selection of optimal frequency sets and polynomial retrieval algorithms for rainfall parameters is carried out and discussed. Ground-based radiometric measurements at 13.0, 23.8, and 31.7 GHz are used for experimentally testing the retrieval algorithms. Comparison with rain-gauge data and rain path-attenuation measurements, derived from the three ITALSAT satellite beacons at 18.7, 39.6, and 49.5 GHz acquired at Pomezia (Rome, Italy), are performed for two selected cases of moderate and intense rainfall during 1998     

Beam-filling effect correction with subpixel cloud fraction using a neural network

In this study, the effects of cloud inhomogeneity on microwave rain rate retrievals are investigated. A single-channel (85 GHz) empirically based algorithm using a neural network approach is presented. The objective is to correct the beam-filling error (BFE), that might occur because of the inherent variability within coarse microwave pixels, with subpixel information. To this aim, we used the Tropical Rainfall Measuring Mission passive microwave, thermal infrared and radar data. The integration of spatial information into the retrieval algorithm enables us to partially overcome the BFE. We use two parameters which characterize the horizontal cloud inhomogeneity within the microwave radiometer field of view, and we add them to simulated brightness temperatures as inputs of the neural network algorithm. The first one is the cloud fraction derived from infrared measurement, and the second corresponds to the fraction of the rainy area derived from radar measurements. The output rain rates were validated using the Precipitation Radar data. It was found that adding cloud fraction of microwave pixels, can lead to more accurate retrievals. Instantaneous precipitation estimates demonstrated correlations of /spl sim/0.6-0.7 and /spl sim/0.7-0.8 with radar-derived rain rates, for ocean and land retrievals respectively. In spite of the problem inherent in deriving the cloud (or rain) fraction, the initial validation results presented in this study are reasonably encouraging and show the advantage of utilizing the information from different sensors in order to optimize the retrieval of rainfall.     

Multivariate statistical integration of Satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale

The objective of this paper is to investigate how the complementarity between low earth orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne IR measurements on sufficiently limited subregions and time windows. The proposed methodologies are focused on new statistical approaches, namely the multivariate probability matching (MPM) and variance-constrained multiple regression (VMR). The MPM and VMR methods are rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the proposed MW-IR combined rainfall algorithm (MICRA), three case studies are discussed, two on a global scale on November 1999 and 2000 and one over the Mediterranean area. A comprehensive set of statistical parameters for detection and estimation assessment is introduced to evaluate the error budget. For a comparative evaluation, the analysis of these case studies has been extended to similar techniques available in literature.     

An analysis of SeaWinds-based rain retrieval in severe weather events

The Ku-band SeaWinds scatterometer estimates near-surface ocean wind vectors by relating measured backscatter to a geophysical model function for the near-surface vector wind. The conventional wind retrieval algorithm does not explicitly account for SeaWinds' sensitivity to rain, resulting in rain-caused wind retrieval error. A new retrieval method, termed "simultaneous wind/rain retrieval," that estimates both wind and rain from rain-contaminated measurements has been previously proposed and validated with Tropical Rain Measuring Mission data. Here, the accuracy of rains retrieved by the new method is validated through comparison with the Next Generation Weather Radar (NEXRAD) in coastal storm events. The rains detected by both sensors are comparable, though SeaWinds-estimated rains exhibit greater variability. The performance of simultaneous wind/rain retrieval in flagging excessively rain-contaminated winds is discussed and compared to existing methods. A new rain-only retrieval algorithm for use in rain-backscatter-dominated areas is proposed and tested. A simple noise model for SeaWinds rain estimates is developed, and Monte Carlo simulation is employed to verify the model. The model shows that SeaWinds rain estimates have a standard deviation of 2.5 mm/h, which is higher than the NEXRAD measurements. Thresholding SeaWinds rain estimates at 2 mm/h yields a better rain flag than current rain flag algorithms.     

Simultaneous wind and rain retrieval using SeaWinds data

The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the "sweet spot" of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.     

Estimation of packet losses due to propagation impairments: Application to TCP performance over satellite

The transmission control protocol (TCP) is widely used to provide reliable data transmission due to its congestion and flow control mechanisms that provide reliable error recovery in higher layers. In satellite links, various atmospheric phenomena may lead to high packet loss rate (PLR) degrading the TCP throughput. Modern satellite systems operate at frequencies above 10 GHz, where rainfall is the dominant fading mechanism leading to high bit error ratio and correlated packet losses. In this paper, a mathematical analysis is presented to accurately describe the statistical properties of the packet‐error process in a dynamically varying satellite channel. The proposed method is extended to provide PLR estimations when block forward error correction (FEC) is employed. A new Markov‐based method, based on the previous analysis and adapted to the rain‐faded satellite channel, is also presented for the estimation of TCP SACK throughput and tested against simulation results. Based on the information provided by the packet‐error model, a study between the TCP performance under various FEC schemes and a proposed adaptive FEC scheme has provided indications about the superiority of the proposed model. Copyright © 2007 John Wiley & Sons, Ltd.     

Spatial scales of tropical precipitation inferred from TRMM microwave imager data

The local spatial scales of tropical precipitating systems were studied using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rate imagery from the TRMM satellite. Rain rates were determined from TMI data using the Goddard Profiling (GPROF) Version 5 algorithm. Following the analysis of Ricciardulli and Sardeshmukh (RS), who studied local spatial scales of tropical deep convection using global cloud imagery (GCI) data, active precipitating months were defined alternatively as those having greater than either 0.1 mm/h or 1 mm/h of rain for more than 5% of the time. Spatial autocorrelation values of rain rate were subsequently computed on a 55/spl times/55 km grid for convectively active months from 1998 to 2002. The results were fitted to an exponential correlation model using a nonlinear least squares routine to estimate a spatial correlation length at each grid cell. The mean spatial scale over land was 90.5 km and over oceans was 122.3 km for a threshold of 0.1 mm/h of rain with slightly higher values for a threshold of 1 mm/h of rain. An error analysis was performed which showed that the error in these determinations was of order 2% to 10%. The results of this study should be useful in the design of convective schemes for general circulation models and for precipitation error covariance models for use in numerical weather prediction and associated data assimilation schemes. The results of the TMI study also largely concur with those of RS, although the more direct relationship between the TMI data and rain rate relative to the GCI imagery provide more accurate correlation length estimates. The results also confirm the strong impact of land in producing short spatial scale convective rain.     

Extension of GPM dual-frequency iterative retrieval method with DSD-profile constraint

A new Dual-Frequency Precipitation Radar (DPR) will be included on the Global Precipitation Measurement (GPM) core satellite which will succeed the highly successful Tropical Rainfall Measuring Mission satellite launched in 1997. New dual-frequency drop-size distribution (DSD) and rain-rate estimation algorithms are being developed to take advantage of the enhanced capabilities of the DPR. It has been shown previously that a backward-iteration algorithm can be embedded within a single-loop feedback model to retrieve the rain rate. However, the single-loop model is unable to correctly estimate DSD profiles for a significant portion of global median-volume-diameter, D/sub o/, and normalized DSD intercept parameter, N/sub w/, combinations in rain because of a multiple-value solution space. For the remaining D/sub o/,N/sub w/ pairs, another retrieval method is necessary. This paper proposes a dual-loop model, in which the intercept parameter, N/sub w/, of the DSD is constrained in its vertical profile, to guide the algorithm to correct convergence. This allows an additional constraint on the DSD values estimated by the iterative algorithm and helps to retrieve correct DSD values in the regions where the iterative approach alone fails. To demonstrate feasibility of the proposed method, three test cases representative of many DSD and profile combinations are discussed. The first case is a constant vertical profile of the DSD parameters. The second case examines linear variation of the DSD parameters, and the third case examines how measurement error affects the retrieval process. In each case, the proposed constraint on the intercept parameter is implemented, and the results are discussed. Using the constraint, the dual-loop algorithm is able to retrieve reasonable values for the DSDs and rain-rate profiles and extend the convergence region of the algorithm.     

Development of a neural network based algorithm for rainfallestimation from radar observations

Rainfall estimation based on radar measurements has been an important topic in radar meteorology for more than four decades. This research problem has been addressed using two approaches, namely a) parametric estimates using reflectivity-rainfall relation (Z-R relation) or equations using multiparameter radar measurements such as reflectivity, differential reflectivity, and specific propagation phase, and b) relations obtained by matching probability distribution functions of radar based estimates and ground observations of rainfall. In this paper the authors introduce a neural network based approach to address this problem by taking into account the three-dimensional (3D) structure of precipitation. A three-layer perceptron neural network is developed for rainfall estimation from radar measurements. The neural network is trained using the radar measurements as the input and the ground raingage measurements as the target output. The neural network based estimates are evaluated using data collected during the Convection and Precipitation Electrification (CaPE) experiment conducted over central Florida in 1991. The results of the evaluation show that the neural network can be successfully applied to obtain rainfall estimates on the ground based on radar observations. The rainfall estimates obtained from neural network are shown to be better than those obtained from several existing techniques. The neural network based rainfall estimate offers an alternate approach to the rainfall estimation problem, and it can be implemented easily in operational weather radar systems     

雨滴谱参数估计优化方案及其微物理资料检验

提出一种新的雨滴谱参数估计优化方案。其最大优点就是：对于任意几束相邻的测雨雷达观测资料,从任意的雨滴谱模型（如Marshall-Palmer经验公式）决定的积分雨参数之间的关系（如k-Z关系）出发,利用雷达视反向率资料并借助雷达海平面或地表面回波信号,可以获取束向上的比较接近实际降雨场的雨滴谱;然后利用校正的雨滴谱参数决定的积分雨参数之间的关系,降雨量反演长法就可以提供较准确的降雨量分布。这个方案可行性被同区域机载2D－P雨滴谱仪实测的微物理资料很好地验证。     

降水对Ku/Ka频段星地链路衰减特性的影响研究

近年来,Ku和Ka卫星在广播、通信和军事等领域应用数量越来越多,传统的Ku和Ka波段星地链路降水衰减预报模型基于经验关系和理想假设,考虑降水微物理特性不足,针对此问题,在实测降水粒子微物理特征的基础上定量分析了降水垂直分布、粒子形状、粒子取向、粒子相态等对Ku和Ka频段信号衰减的影响特性.结果表明,与考虑降水非均匀垂直分布的计算结果相比,ITU和SAM模型是基于降水分布均匀的假设,无法代表降水垂直分布不均匀时的衰减情况;降水粒子形状和取向对衰减的影响较小,在13GHz和32GHz频段,球形和非球形粒子衰减值的平均绝对偏差均在0.01dB以下,不同粒子取向时衰减系数的平均绝对偏差最大值为0.00098dB/km和0.0207dB/km;不同相态的降水引起的衰减差异较大,衰减值从大到小依次是湿雪、水和冰.研究结果可以为Ku和Ka波段星地链路传播特性评估及降雨反演新方法提供基本的理论支撑和数据参考.     

2D DOA and Frequency Estimation Method with One Vector-Sensor and Two Pressure Sensors Based on ESPRIT and Signal Power

We use one vector and two pressure sensors to form a sparse large aperture L-shape array for high performance two-dimensional (2D) direction of arrival (DOA) and frequency estimation. Because the number of sensors is small and there is only one vector sensor in the presented array, thus, the installation of sensors in the array is simpler and installation error is smaller, than the conventional array. Meanwhile, a high performance 2D DOA and frequency estimation method is presented. Firstly, utilizing single vector sensor and based on the ESPRIT, a group coarse 2D DOA and frequency parameters are obtained. Secondly, to restrain space noise or interference, a matrix filter is utilized to process the covariance matrix which comes from sensor array, so as to form a new covariance matrix which possesses high signal to noise ratio. Thirdly, utilizing the new covariance matrix and based on the ESPRIT again, accurate but ambiguity angles estimates are obtained. Fourthly, one signal power estimator and one optimization method are presented to solve the angle ambiguity and frequency ambiguity problems, respectively. The proposed method gains a high performance 2D DOA and frequency estimation results. Numerical simulations are performed to verify the feasibility of the proposed method.

     

Rain Rate Statistics and Fade Distribution of Millimeter Waves in Indian Continents

For any communication service operating in the Microwave/ Millimeter wave region, statistical information characterising the attenuation due to rain along satellite slant path would be required for the design of satellite communication links and for the broadcasting network above 20 Ghz. It is necessary to have a prior knowledge of the probability of exceeding different levels of rain attenuation in order to design appropriate fade margins into systems and establishing estimates of the year to year variability of rain fade margin for particular geographic regions of India so that the communication system reflects the extremes of these variabilities. Direct measurement of beacon signals from geostationary satellites have been a mean to determine the above information and experiments can be pursued with satellite such as INSAT. [1] Attenuatiuon of Millimeter Waves by rainfall restricts the path length of a communication system. A knowledge of the rain attenuation at such frequencies is therefore desirable in designing a reliable communication system. Signal level fading over line-of-sight links strongly depends on the hop length, frequency and climate. For short hops, the probability of occurance of deep fades becomes diminishingly small. However, since an extended hop length is possible for regions with little rain activity, clear weather fading can affect the link reliability in a similar way ti a rain.[2]     

Diversity improvement estimation from rain Radar databases using maximum likelihood estimation

This research examines route diversity as a fade mitigation technique in the presence of rain, for terrestrial microwave links. The improvement in availability due to diversity depends upon the complex spatio-temporal properties of rainfall. To produce a general model to predict the advantage due to route diversity it is necessary to be able to predict the correlation of rain attenuation on arbitrary pairs of microwave links. This is achieved by examination of a database of radar derived rain rate fields. Given a representative sample of rain field images, the joint rain attenuation statistics of arbitrary configurations of terrestrial links can be estimated. Existing rain field databases often yield very small numbers of high joint attenuation events. Consequently, estimates of the probability of joint high attenuation events derived from ratios of the number of occurrences can be highly inaccurate. This paper assumes that pairs of terrestrial microwave links have joint rain attenuation distributions that are bi-lognormally distributed. Four of the five distribution parameters can be estimated from ITU-R models. A maximum likelihood estimation (MLE) method is used to estimate the fifth parameter, i.e., the covariance or correlation. The predicted diversity statistics vary smoothly and yield plausible extrapolations into low probability situations.     

Tests of sequential data assimilation for retrieving profile soilmoisture and temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses     

A neural network approach to estimating rainfall from spacebornemicrowave data

Various techniques use microwave (MW) brightness temperature (BT) data, obtained from remote sensing orbiting platforms, to calculate rain rates. The most commonly used techniques are based on regressions or other statistical methods. An emerging tool in rainfall estimation using satellite data is artificial neural networks (NNs), NNs are mathematical models that are capable of learning complex relationships. They consist of highly interconnected, interactive data processing units. NNs are implemented in this study to estimate rainfall, and backpropagation is used as a learning scheme. The inputs for the training phase are BTs and the outputs are rainfall rates, all generated by three-dimensional (3D) simulations based on a 3D stochastic, space-time rainfall model, and a 3D radiative transfer model. Once training is complete the NNs are presented with multi-frequency and polarized (horizontal and vertical) BT data, obtained from the Special Sensor Microwave/Imager (SSM/I) instrument onboard the F10 and F11 polar-orbiting meteorological satellites. Hence, rainrates corresponding to real BT measurements are generated. The rainfall rates are also estimated using a log-linear regression model. Comparison of the two approaches, using simulated data, shows that the NN can represent more accurately the underlying relationship between BT and rainrate than the regression model, Comparison of the rates, estimated by both methods, with radar-estimated rainrates shows that NNs outperform the regression model. This study demonstrates the great potential of NNs in estimating rainfall from remotely sensed data     

Estimation of dense image flow fields in fluids

The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualization of cloud or sea ice movements in sequences of crude temporal sampling, a satisfactory nonblurred temporal interpolation can be performed only when the flow field or an estimate thereof is known. Estimated flow fields in weather satellite imagery might also be used on an operational basis as inputs to short-term weather prediction. The authors describe a method for the estimation of dense flow fields. Local measurements of motion are obtained by analysis of the local energy distribution, which is sampled by using a set of three-dimensional (3D) spatio-temporal filters. The estimated local energy distribution also allows the authors to compute a confidence measure of the estimated local normal flow. The algorithm, furthermore, utilizes Markovian random fields in order to integrate the local estimates of normal flows into a dense flow field by using measures of spatial smoothness. To obtain smoothness, the authors will constrain first-order derivatives of the flow field. The performance of the algorithm is illustrated by the estimation of the flow fields corresponding to a sequence of Meteosat thermal images. The estimated flow fields are used in a temporal interpolation scheme     

海上云层和降水对船载高频段卫星通信的影响

介绍了海洋云层和降水的特点以及云层和降水对电波吸收的影响;描述了海洋层状雨和对流雨的降雨空间结构状态以及对高频段卫星通信雨衰的影响;分析了高频段卫星通信雨衰原因,指出了电磁波吸收、热噪声和去极化是影响高频段雨衰的重要方面,并针对海上工作环境特点提出了上行功率控制、频率分集、速率分集、自适应调制等抗雨衰方法.