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A simplistic approach has been proposed to estimate annual rainfall amount from cloud liquid water content and precipitable water content utilizing the data pertaining to the period of 1997–2006. The study involves seven land locations over India, seven stations over plateau and seven locations over the Indian Ocean. The wavelet analyses exhibit prominent annual peaks in the global spectra of rainfall, cloud liquid water content and precipitable water content. Power–law relationships are found to exist between the global wavelet peaks of precipitation and those of both the parameters, namely, cloud liquid water content and precipitable water content. Again, a linear relationship exists between global wavelet peaks of rainfall amount and total rainfall amount. The rainfall estimations utilizing cloud liquid water content data exhibit better matching with the measured values than those utilizing precipitable water content data. [ABSTRACT FROM AUTHOR]

The forecast of debris flow occurrence relies mainly on empirical rainfall intensity–duration thresholds, which are based on rain gauge observations. This work focuses on the effect of rainfall estimation uncertainty on the estimation of debris flow triggering rainfall events and on the identification of rainfall thresholds for debris flow occurrence. Specifically, the influence of rain gauge network density and the interpolation method on the estimation of debris flow triggering rainfall is investigated. These questions are examined using high-resolution, carefully corrected radar data to represent space–time patterns of true precipitation at the debris flow initiation points and in the surrounding area. Radar rainfall fields are sampled by simulated rain gauge networks, stochastically generated with varying rain gauge densities. Based on these networks, rainfall is estimated by using three rainfall interpolation methods: nearest neighbor (NN), inverse distance weighting (IDW) and ordinary kriging (OK). Results show that NN provides the estimates with bias smaller than IDW and OK but larger estimation variance. Overall, decrease in gauge density leads to increased underestimation and increased estimation variance of debris flow triggering rainfall. Rainfall estimation error leads to large underestimation of the intensity–duration thresholds. However, comparison of results shows that no particular benefit in intensity–duration threshold estimation is obtained by using approaches that are more complex than the NN method. [ABSTRACT FROM AUTHOR]

This paper assessed the SMOS soil moisture values from Level 3 (SMOS L3SM) product provided by the French CNES-CATDS. The evaluation was conducted at the local scale through comparison with ground-based soil moisture measurements acquired in Mali, Niger and Benin from 2010 to 2012. The SMOS L3SM product was compared to three other satellite-based soil moisture products. It was found that, in average over the three sites, the SMOS L3SM product provided the best coefficients of correlation and the lowest root mean square errors (RMSE). The second part of the paper is devoted to retrieve soil moisture estimates between successive SMOS soil moisture measurements in order to increase the temporal resolution. The result of the methodology allows obtaining 3-hour soil moisture mapping over West Africa with a coefficient of correlation greater than 0.82, and an RMSE lower than 0.030 m 3 m − 3 in Niger and Mali and lower than 0.044 m 3 m − 3 in Benin. [ABSTRACT FROM AUTHOR]

Accurate rainfall estimations based on ground-based rainfall observations and satellite-based rainfall measurements are essential for hydrological and environmental modeling in the Qaidam Basin of China. We evaluated the accuracy of daily and monthly scale Tropical Rainfall Measuring Mission (TRMM) rainfall products in the Qaidam Basin. A Geographically Weighted Regression (GWR) was used to estimate the spatial distribution of the TRMM product error using altitude and geographical latitude and longitude as independent variables. Finally, a rainfall model was developed by combining ground-based and satellite-based rainfall measurements, and the model precision was validated with a cross-validation method based on rainfall gauge measurements. The TRMM precipitation observations may contain errors compared with the ground-measured precipitation, and the error for daily data was higher than that for monthly data. A time series of TRMM rainfall measurements at the same location showed errors at certain time intervals. The ground-based and satellite-based rainfall GWR model improved the error in the TRMM rainfall products. This rainfall estimation model with a 1-km spatial resolution is applicable in the Qaidam Basin in which there is a sparse network of rainfall gauges, and is significant for spatial investigations of hydrology and climate change.

Summary This work aims to evaluate the potential benefits and limitations of radar rainfall estimates for the identification of debris flow occurrence rainfall thresholds. Observations from a C-band weather radar and raingauge data are analyzed for seven convective rainfall events that triggered 117 debris flows in the Upper Adige river basin (Eastern Italian Alps). Four radar rainfall scenarios characterized by incrementally increasing accuracy are used for the estimation of rainfall intensity–duration thresholds. Error sources considered in the radar correction chain include beam blockage, attenuation and vertical profile of reflectivity. The impact of rainfall accuracy on the estimation of the intensity–duration thresholds is analyzed by comparing the thresholds derived from the various radar rainfall scenarios, using the rainfall estimates obtained from the application of the complete correction algorithm as a reference scenario. Results show that the application of the complete correction algorithm improves significantly the accuracy of radar rainfall estimates: Fractional Standard Error is decreased by 20%, Correlation Coefficient is increased by 24% relative to uncorrected data. Correction for atmospheric attenuation is the most important step in the correction chain. The use of uncorrected radar estimates leads to substantially underestimated thresholds with respect to the reference scenario; adjusting radar data for bias is not sufficient to overcome this problem. Radar rainfall estimates which are corrected but not adjusted with raingauge data are able to provide intensity–duration thresholds which are almost indistinguishable from the reference scenario. The derivation of the radar-based threshold is shown to be very sensitive to spatial location errors of rainfall and debris flows. Raingauge-based thresholds are severely underestimated with respect to the reference scenario. This clearly demonstrates the severity of the raingauge-based estimation problem for the derivation of debris flow triggering rainfall and highlights the benefits of using weather radar observations, at least for the case of short duration convective storms. [ABSTRACT FROM AUTHOR]

Rainfall, an integral component of the global water and energy cycle, is one of the critical weather elements. Reliable information of rainfall over India is crucial for food security and sustainable economic growth. The first Indian dedicated meteorological geostationary satellite Kalpana-1 was launched by the Indian Space Research Organisation in late 2002 to study the synoptic weather systems, monsoons and extreme weather events. Various geophysical parameters derived from this satellite measurements are operational and used for a wide range of applications. Two rainfall products, based on distinct algorithms, from this satellite are also available to users. These two algorithms after certain refinements are also applied to the recently launched INSAT-3D satellite measurements to estimate rainfall. In this article, the algorithms used for the development of these Kalpana-1-based rainfall products are summarized. The assessment of these rainfall products against standard multisatellite datasets and in situ observations are also outlined. Both the rainfall products are comparable with independent multisatellite datasets and have reasonable agreement with ground-based observations over the Indian land and oceanic regions. Limitations of these rainfall products are also presented; and future scope for further refinement of these products in perspective of upcoming Indian geostationary satellite missions is proposed. [ABSTRACT FROM AUTHOR]

ABSTRACT The empirical relationships used to estimate rainfall amounts from polarimetric radar observations were developed and tested by using long-period drop size distribution ( DSD) data and monitoring of rainfall events prior to operational use in Korea. Rainfall associated with Typhoon Meari in 2011 was selected to assess the performance of these relationships for point and areal rainfall amounts. Data quality was checked in regions of light rain to enable the quantitative use of polarimetric variables. The distributions of the cross-correlation co-efficient and standard deviation of the differential phase shift agreed with those established in a previous study, but the absolute average deviation of differential reflectivity ( ZDR) was a little distorted. Biases in reflectivity ( Z) and differential reflectivity were calculated following established methods and found to be -0.18 and 0.47 dB, respectively. The accuracy of rainfall amounts calculated from R( KDP) and R( KDP, ZDR) was poor. The best estimates of rainfall were obtained using R(Z, ZDR) based on DSDs from Oklahoma ( OKC) in the USA and Busan ( BSC) for both the point and areal mean cases. Correlation co-efficients of R(Z, ZDR) using the BSC DSDs were better than those using the OKC DSDs for areal mean rainfall amounts. Rainfall amounts in this particular case in Korea were estimated more accurately using the Brandes drop shape for R(Z, ZDR) than the equilibrium drop shape. [ABSTRACT FROM AUTHOR]

Estimation of rainfall intensity-duration thresholds, used for the identification of debris flow/landslide triggering rainfall events, has been traditionally based on raingauge observations. The main drawback of using information from gauges is that the measurement stations are usually located far away from the debris flow initiation areas. In complex terrain where debris flows take place, the spatial variability of rainfall can be very high and this translates in large uncertainty of raingauge-based estimates of debris flow triggering rainfall. This work focuses on the assessment of the impact of rainfall estimation uncertainty on identification and use of rainfall thresholds for debris flow occurrence. The Upper Adige River basin, Northern Italy, is the area of study. A detailed database of more than 400 identified debris flow initiation points during the period 2000-2010 and a raingauge network of 100 stations comprise the database used for this work. The methodology examines the intensity-duration thresholds derived from a set of raingauges that are assumed to be located at debris flow initiation points (DFRs) and an equivalent set of raingauges assumed to have the role of the closest (to debris flow) available measurement (MRs). A set of reference rainfall thresholds is used to identify the rainfall events at DFRs that "triggered" debris flows (i.e. exceed the threshold). For these same events, the corresponding rainfall thresholds are derived from MR observations. Comparison between the rainfall thresholds derived from DFRs and MRs revealed that uncertainty in rainfall estimation has a major impact on estimated intensity-duration thresholds. Specifically, the results showed that thresholds estimated from MR observations are consistently underestimated. Evaluation of the estimated thresholds for warning procedures showed that while detection is high, the main issue is the high false alarm ratio, which limits the overall accuracy of the procedure. Overall performance on debris flow prediction was shown to be good for low rainfall thresholds and poor for high rainfall thresholds examined. Lastly, it was found that filtering out rainfall events with duration <12h may improve bias in estimated thresholds and performance for high rainfall thresholds. [ABSTRACT FROM AUTHOR]

In this paper, an improved Kalpana-1 infrared (IR) based rainfall estimation algorithm, specific to Indian summer monsoon region is presented. This algorithm comprises of two parts: (i) development of Kalpana-1 IR based rainfall estimation algorithm with improvement for orographic warm rain underestimation generally suffered by IR based rainfall estimation methods and (ii) cooling index to take care of the growth and decay of clouds and thereby improving the precipitation estimation. In the first part, a power-law based regression relationship between cloud top temperature from Kalpana-1 IR channel and rainfall from Tropical Rainfall Measuring Mission (TRMM) - precipitation radar specific to the Indian region is developed. This algorithm tries to overcome the inherent orographic issues of the IR based rainfall estimation techniques. Over the windward sides of the Western Ghats, Himalayas and Arakan Yoma mountain chains, separate regression coefficients are generated to take care of the orographically produced warm rainfall. Generally global rainfall retrieval methods fail to detect the warm rainfall over these regions. Rain estimated over the orographic region is suitably blended with the rain retrieved over the entire domain comprising of the Indian monsoon region and parts of the Indian Ocean using another regression relationship. While blending, a smoothening function is applied to avoid rainfall artefacts and an elliptical weighting function is introduced for the purpose. In the second part, a cooling index to distinguish rain/no-rain conditions is developed using Kalpana-1 IR data. The cooling index identifies the cloud growing/decaying regions using two consecutive half-hourly IR images of Kalpana-1 by assigning appropriate weights to growing and non-growing clouds. Intercomparison of estimated rainfall from the present algorithm with TRMM-3B42/3B43 precipitation products and Indian Meteorological Department (IMD) gridded rain gauge data are found to be encouraging. The advantages of the present algorithm are that it requires only two IR images as input without depending on other sources of information and simple to implement. The present algorithm performs better than the existing Kalpana-1 IR based rainfall estimation algorithm. Comparison with IMD rainfall data suggests that the underestimation of average rainfall has decreased by 30% for the present algorithm. [ABSTRACT FROM AUTHOR]

Commercial wireless microwave links have been recently proven to be an effective tool for precipitation monitoring, mainly for accurate rainfall estimation and high-resolution rainfall mapping. This paper focuses on the challenge of precipitation classification from the measurements of received signal level (RSL) in several commercial wireless microwave links, by suggesting a tree of classification based on the physical features that distinguish between different phenomena. Wet periods are first identified, followed by a classification of the wet periods into pure rain or sleet. The classification is based on the kernel Fisher discriminant analysis, followed by a decision-making process. The suggested procedure is tested on real data, and its performance is evaluated. It is shown that the proposed classification is in very good agreement (85%) with that of a special-purpose meteorological device called disdrometer. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present two model-based methods to estimate areal rainfall in urban catchments. [•] Both methods provide realistic estimates of areal rainfall intensities. [•] The two methods rely on different concepts and have different data requirements. [•] The reverse model propagates and amplifies uncertainties in measured runoff. [•] In both methods model parameter uncertainty influences rainfall estimates. [Copyright &y& Elsevier]

In support of SNOW-V10, the National Oceanic Administration/National Severe Storms Laboratory (NOAA/NSSL) mobile dual-polarized X-band (NO-XP) radar was deployed to Birch Bay State Park in Birch Bay, Washington from 3 January 2010 to 17 March 2010. In addition to being made available in real time for Science and NOWcasting of the Olympic Weather for Vancouver 2010 (SNOW-V10) operations, NO-XP data are used here to demonstrate the capabilities of easily deployable, polarimetric X-band radar systems, especially for regions where mountainous terrain results in partial beam blockage. A rainfall estimator based on specific attenuation is shown to mitigate the effects of partial beam blockage and provide potential improvement in rainfall estimation. The ability of polarimetric X-band radar to accurately detect melting layer (ML) height is also shown. A 16 h comparison of radar reflectivity ( Z), differential reflectivity ( Z), and correlation coefficient (ρ) measurements from NO-XP with vertically pointing Micro Rain Radar observations indicates that the two instruments provide ML height evolution that exhibit consistent temporal trends. Since even slight changes in the ML height in regions of mountainous terrain might result in a change in precipitation type measured at the surface, this shows that horizontally extensive information on ML height fluctuations, such as provided by the NO-XP, is useful in determining short term changes in expected precipitation type. Finally, range-height indicator (RHI) scans of NO-XP Z, Z, and ρ fields from SNOW-V10 are used to demonstrate the ability of polarimetric radar to diagnose microphysical processes (both above and below the ML) that otherwise remain unseen by conventional radar. Near-surface enhancements in Z are attributed to either differential sedimentation or the preferential evaporation of smaller drops. Immediately above the ML, regions of high Z, low Z, and high ρ are believed to be associated with convective turrets containing heavily aggregated or rimed snow that supply water/ice mass that later result in enhanced regions of precipitation near the surface. Higher up, horizontally extensive regions of enhanced Z are attributed to rapid dendritic growth and the onset of snow aggregation, a feature that has been widely observed with both S band and C band radars. [ABSTRACT FROM AUTHOR]

Abstract: In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well. [Copyright &y& Elsevier]

A network of four weather radars was deployed as part of the Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA). The radars operate at the X-band frequency and are capable of polarimetric measurements. The rainfall polarimetric self-consistency principle for absolute calibration is evaluated and applied to the CASA radars' data to estimate any bias in the reflectivity (Z) measurements. Moreover, prior to the application of the self-consistency principle, bias error correction of differential reflectivity (ZDR) measurements is required. Two different approaches were evaluated for ZDR bias correction, the dry aggregated snow approach and the light rain approach. Results of ZDR calibration show an accuracy of 0.2 dB or better in bias estimation for the two events analyzed when both methods are compared. Moreover, results show a calibration accuracy of 0.6 dB or better for the Z bias estimated using the self-consistency principle. For verification of results, Z bias estimates from the self-consistency principle are compared with Z bias estimated from the comparison between the CASA X-band and the two nearby WSR-88D S-band radars' data. Comparison of the two approaches shows a difference in the Z bias estimation of 0.61 dB or better, which validates the use of the self-consistency principle in rainfall for the absolute radar calibration of the CASA radars. Results show that the self-consistency principle provides a means to estimate bias errors in the radar power measurements from a specific observed medium with a set of space-time characteristics that are not taken into account in the radar hardware relative calibration. [ABSTRACT FROM AUTHOR]

A prompt availability of precipitation intensity is crucial for accurate and effective flood forecast. When radars are coordinated, the mosaic of weather radar can perform rainfall estimation over large areas. This study presents an experimental method of rainfall estimation based on multiple-Doppler radars over the Huaihe River Basin in China. The method emphasizes the combined use of six radars in the area. The processes reviewed are base-data collection, conversion from radar reflectivity to rain rate, rain gauge calibration, consensus of rainfall, and rainfall mosaics. A case study of an extreme meteorological and hydrological event, which occurred in July 2007 over the Huaihe River basin in China, was examined, and the estimated error of rainfall is lower than 45% after calibration with measurements from rain gauges. The results show that the proposed rainfall estimation method presents a cumulative rainfall distribution that is similar to the observations. It is both practical and reliable for the Huaihe River Basin. [ABSTRACT FROM AUTHOR]

The ability to predict the runoff response of an urban catchment to rainfall is crucial for managing drainage systems effectively and controlling discharges from urban areas. In this paper we assess the potential of commercial microwave links (MWL) to capture the spatio-temporal rainfall dynamics and thus improve urban rainfall-runoff modelling. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge (RG) observations. In a case study, we are able to show that MWL networks in urban areas are sufficiently dense to provide good information on spatio-temporal rainfall variability and can thus considerably improve pipe flow prediction, even in small subcatchments. In addition, the better spatial coverage also improves the control of discharges from urban areas. This is especially beneficial for heavy rainfall, which usually has a high spatial variability that cannot be accurately captured by RG point measurements [ABSTRACT FROM AUTHOR]

Some studies have proposed the estimation of convective rainfall from lightning observations by the computation of the rainfall-lightning ratio ( RLR). However, as such ratio may depend on season, convective regime and other factors, known approaches failed to provide values of RLR with low variability. An accurate RLR would allow estimating rainfall from lightning data in areas that lack weather radar coverage. This work proposes a straightforward approach for the computation of RLR, based on a temporal sliding-window and a fitting function. It was tested for thunderstorms observed in the Southeastern Brazil with good results. [ABSTRACT FROM AUTHOR]

The detection of rainfall remains a challenge for the monitoring of precipitation from space. A methodology is presented to identify rain events from spaceborne passive microwave data using neural networks. We focus on BRAIN, the algorithm that provides instantaneous quantitative precipitation estimates at the surface, based on the MADRAS radiometer onboard the Megha-Tropiques satellite. A version of BRAIN using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been used to compare several multilayer perceptrons (MLP) trained on different combinations of TMI brightness temperatures with the conventional GSCAT-2 algorithm approach used for rainfall detection. These classifiers were compared at a global scale to reference values from the TRMM Precipitation Radar (PR). They were also compared to ground measurements using two 1° × 1° dense rain-gauge networks from different climatic zones in West Africa to assess the influence of rainfall types. At the global scale the MLPs provide better Probability of Detection than the GSCAT-2 decision tree but tend to have a higher False Alarm Rate. While no unique solution exists given the strong regional dependence of the classifiers' performances, the screen based on the 19, 21 and 85 GHz channels provides the best detection results at the instantaneous scales. As to accumulated rainfall, the screen that exhibits the lower bias relative to the PR makes use of the 37 and 85 GHz channels. The evaluation over West Africa using 10 years of TRMM overpasses shows that MLPs are in better agreement with both the PR and the gauges than GSCAT-2. The MLP trained on the 37 and 85 GHz channels increases the Probability of Detection by nearly 35% compared to the former screening over the two studied regions. Better results are obtained in the case of organized systems. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Summary: We describe a method for estimating the daily, spatially distributed recharge of aquifers in mountainous karst terrains using a water balance. Water recharge into an aquifer is a highly variable process over both time and space. Many methods have been developed to assess aquifer recharge although most have been applied to detrital aquifers. Many karst aquifers, especially in Mediterranean areas, occur in mountainous environments where rainfall and evapotranspiration can vary considerably over space and time and where there are usually few rainfall and temperature monitoring stations. We have used an advanced geostatistical method to estimate daily rainfall and temperature. The method involves kriging with an external drift using a climatological semi-variogram model inferred by modified maximum-likelihood. The depth of the soil–epikarst layer is estimated from remote sensing and terrain analysis data together with field observations and electrical resistivity tomography. Because of the karst nature of the mountainous terrain, concentrated infiltration is allowed for in some places. The parameters are calibrated against the cumulative discharge of various springs. The method is illustrated by a case study of the Sierra de las Nieves aquifer in the mountainous karst region of southern Spain. [Copyright &y& Elsevier]

The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation, in this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail. [ABSTRACT FROM AUTHOR]

The paper presents a comparative analysis between several expert systems built for rainfall estimation from reflectivity of radar observations and ALADIN model parameters. The systems were built using Artificial Neural Networks (ANNs) and are dedicated to the estimation of the rainfall rate. The main advantage of ANNs is in cases where intrinsic nonlinearities in the dynamics prevent the development of exactly solvable models. In meteorology, all of these criteria are present in the sense that the dynamics are inherently nonlinear, and prediction is one of the main goals. Two types of expert systems were built: pure ANN systems which use as inputs reflectivity data and ALADIN model parameters, and hybrid ANN systems which use as inputs a concatenated database obtained by the original data i.e. reflectivity data and ALADIN model parameters. The WSR-98 D S-band Doppler radar located in Barnova, in the northeast of Romania, recorded the radar data. The validation results of all expert systems are analyzed. [ABSTRACT FROM AUTHOR]

The impact of rainfall interpolation techniques and unit hydrograph estimation has been explored for four gauged locations in the Brahmani basin in east India. The use of ground-based and satellite-based data, coupled with testing two interpolation techniques (Thiessen polygon and inverse distance weighting), can yield improved rainfall estimates and fits to observed flows. Due to the presence of significant errors in the areal rainfall estimate it was found that identification of known errors in rainfall data can assist in focusing model calibration on catchment response, thereby reducing the uncertainty in model parameter values. Similarly, using several approaches to estimate the unit hydrograph can assist in reducing uncertainty. The resulting performance of the model for the gauged sites in the Brahmani basin gave Nash-Sutcliffe efficiency (NSE) values for the calibration period of 0.6-0.7. For this basin, the inverse distance weighting approach corrected for spatial variation in rainfall distribution generally gave the best fits to the observed streamflow. Sensitivity to errors in the rainfall surface limits the applicability for this approach in modelling the flows in ungauged basins, however. [ABSTRACT FROM AUTHOR]