Rain Gauge Locations Research Articles

Spatial variability of daily summer rainfall at a local-scale in a mountainous terrain and humid tropical region

The authors study the spatial variability of daily rainfall in a mountainous terrain and tropical humid region within a grid of 6km×6km using daily rainfall data from 22 non-recording rain gauges over a period of 39days. Results indicate that (1) the daily coefficient of variation varied from 15% to 53%, and (2) among all, five of the rain gauge locations consistently gave estimates of the spatial daily mean rainfall within root mean square error of 20%, ten locations gave estimates with root mean square errors ranging from 20% to 30%, and the remaining seven locations gave estimates with root mean square error ranging from 30% to 41%. These levels of errors must be acknowledged when using single rain gauges to estimate local-scale spatial means at the daily scale. This also indicates that there are temporally stable rain gauge locations that give consistently lower errors in spatial mean estimates. Such short-term field campaigns can be used to identify time stable rain gauge locations for rain gauge network design.

Hydro-NEXRAD: metadata computation and use

To enable the efficient use of the Hydro-NEXRAD system in hydrologic research, the authors are developing and computing a set of metadata. Vast archives of radar-centric NEXRAD data, the highly variable spatial and temporal characteristics of precipitation events, and the existence of anomalous data complicate the process of selecting subsets of the data for specific investigation. Well-defined, pre-computed, radar-centric, basin-centric and point (for a selected set of rain gauge locations) metadata offer the possibility of efficiently selecting data subsets that meet user-specified criteria. Metadata have to be computed quickly so that they do not slow down the data ingest process, but they do not have to be highly accurate. Separating rainfall from clear air conditions and heavy rainfall from light precipitation is often sufficient. In this paper, the authors discuss the technical challenges of computing metadata for the vast archives of NEXRAD data and basic data quality control incorporated into that process. Users of Hydro-NEXRAD will be able to query the database using different criteria based on the metadata or visually inspect the time series of the metadata.

To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

In urban drainage modelling long-term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties with regards to long-term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO volumes. Traditionally, long-term rainfall series, from a local rain gauge, are unavailable. In the present case study, however, long and local rain series are available. 2 rainfall gauges have recorded events for approximately 9 years at 2 locations within the catchment. Beside these 2 gauges another 7 gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity can be handled, e.g. by introducing an "averaging procedure" based on the variability within the set of statistics. All simulations are performed by means of the MOUSE LTS model.

Application of Weather Radar in Estimation of Bulk Atmospheric Deposition of Total Phosphorus Over Lake Simcoe

The decline of Lake Simcoe water quality has been attributed to high phosphorus inputs that result in excessive algae and macrophyte growth subsequently contributing to end-of-summer hypolimnetic dissolved oxygen depletion and loss of fish habitat. Out of the estimated 53 to 67 tonne/annum (1998 to 2004 water years) of phosphorus entering the lake, atmospheric deposition is believed to be responsible for 16 to 38 tonne/annum. Historical estimates for atmospheric deposition involved averaging rain gauge (rainfall depth) and rain quality (phosphorus concentration) station data. Through use of this procedure, any spatial variability in the data (quality and quantity) is lost as each gauge is given an equal weighting. This study proposes a methodology to use Next Generation Radar (NEXRAD) to spatially represent rainfall data and a method to correct radar-rainfall estimates to rainfall recorded by local rain gauges. From this analysis it was found that the radar generally represented localized rainfall well, with the majority of correlation coefficients (R2) being over 0.90. Radar related issues that resulted in poor R2 values included virga, overshooting beam, beam attenuation, range related issues and ground clutter. For large bulk atmospheric total phosphorus (TP) deposition events the dominant parameter in calculating TP loads was rainfall depth. Results from this analysis demonstrated a large (−88% to +44%) difference between historical and revised estimates of bulk atmospheric deposition of phosphorus over Lake Simcoe.

Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study.

An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions

The measurement of precipitation is fundamental to our understanding of the hydrological cycle. Increasingly, there exists the capacity to independently determine components of the hydrological cycle from remote sensing data. Developing techniques to combine effectively the multiple streams of information required for a water budget assessment provides a difficult challenge, particularly given the disparities in spatial and temporal scales between measurements and predictions. Two research groups, the Naval Research Laboratory Monterey (NRL) and the University of Arizona (UA), are using a combination of geostationary infrared and polar-orbiting microwave satellite data to derive 6-hourly precipitation estimates over a global 0.25° grid. We examine the performance of these two algorithms for estimating the 24-h rainfall accumulation over India and Sri Lanka for the years 2002 and 2003. The derived values are compared with observations from a network of 39 national weather stations. In addition, two locations, Minicoy in the Laccadive Islands and Port Blair in the Andaman Islands were selected as being representative of the ocean environment to compare these satellite rainfall products against measurements from local rain gauges and Tropical Rainfall Measuring Mission (TRMM) satellite data. The NRL technique was accurate to within 25% of observed precipitation for only 33% of station locations, while the UA technique was accurate to within 25% of observed precipitation for about 47% of station locations.

Correcting of real-time radar rainfall bias using a Kalman filtering approach

This paper presents a method to adjust the mean field bias of radar rainfall estimates in real time. The underlying philosophy for estimation of radar rainfall in real-time used in this study is that the estimated radar rainfall must first be corrected for the reflectivity measurement errors and the Z– R conversion errors based on the physical methods, and thereafter a statistical method is used to remove the average difference (mean field bias) between radar estimates at the rain gauge locations and the corresponding gauge rainfall amounts. Kalman filtering techniques are used as the basis for correcting the mean filed bias in real time. The logarithmic mean field radar rainfall bias is modelled as an Autoregressive order 1 process having a stationary variance. The observation error variance is estimated from a difference between a variance of the observed mean field bias and the stationary variance of the logarithmic bias process. The variance of the observed mean field bias is estimated using a radar rainfall error variance model which depends on (a) the number of rain gauges that measure non-zero rainfall at that hour; (b) the location of these rain gauges; (c) the conditional mean of rain gauge rainfall; and (d) the number of pulses used for reflectivity measurements. A 7-month radar and rain gauge data record from the Kurnell radar in Sydney, Australia are used to test the efficiency of the proposed method. The results shown that the Kalman filtering approach outperforms the use of sample bias correction method when rain gauge density is less than about 1 gauge per 90 km 2 for both climatological and stratiform rainfall, and has a higher accuracy compared to the case where no bias correction is performed, irrespective of the number of rain gauges used in the calibration.

Appropriate Spatial Sampling of Rainfall or Flow Simulation/Echantillonnage Spatial de la Pluie Approprié pour la Simulation D'écoulements

The objective of this study is to find the appropriate number and location of raingauges for a river basin for flow simulation by using statistical analyses and hydrological modelling. First, a statistical method is used to identify the appropriate number of raingauges. Herein the effect of the number of raingauges on the cross-correlation coefficient between areally averaged rainfall and discharge is investigated. Second, a lumped HBV model is used to investigate the effect of the number of raingauges on hydrological modelling performance. The Qingjiang River basin with 26 raingauges in China is used for a case study. The results show that both cross-correlation coefficient and modelling performance increase hyperbolically, and level off after five raingauges (therefore identified to be the appropriate number of rain-gauges) for this basin. The geographical locations of raingauges which give the best and worst hydrological modelling performance are identified, which shows that there is a strong dependence on the local geographical and climatic patterns.

Simulations of deep convection in the Mediterranean area using 3DVAR of conventional and non-conventional data

Abstract. In autumn deep convection in the Mediterranean region is a common phenomenon. The local events characterized by deep convection are still a difficult task even for high resolution numerical weather prediction. Three flood cases, produced by convection either embedded in a large scale system or locally developed, occurring in Italy, are presented. All these case were not correctly forecasted: Sardinia (Cagliari, 13 November 1999); Calabria (Soverato, 7 September 2000) and Sicily (Catania, 16 September 2003). The first case occurred during the Mesoscale Alpine Programme (MAP) campaign, therefore a lot of data are available; for the second one only data from SSM/I and local rain-gauge are available; the third one occurred during the operational experimentation of the TOUGH project. The last one was not well predicted even using the operational assimilation of ground based GPS. To improve the forecast of these cases the assimilation of several data is tested. The variational assimilation performed using 3DVAR of GPS, SSM/I and surface and upper air data is applied to improve the Initial Conditions of the Sicily case. The Sardinia case is improved using either GPS and surface data, whereas for the Soverato case only ZTD is assimilated. The experiments are performed using the MM5 model from Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR); the model is initialized using the new Initial Conditions produced by the variational assimilation of conventional and non conventional data. The results show that the assimilation of the retrieved quantities does produces large improvement in the precipitation forecast. Large sensitivity to the assimilation of surface data and brightness temperature from SSM/I is found.

Application of GIS for processing and establishing the correlation between weather radar reflectivity and precipitation data

AbstractCorrelation between weather radar reflectivity and precipitation data collected by rain gauges allows empirical formulae to be obtained that can be used to create continuous rainfall surfaces from discrete data. Such surfaces are useful in distributed hydrologic modelling and early warning systems in flood management. Because of the spatial relationship between rain gauge locations and radar coverage area, GIS provides the basis for data analysis and manipulation. A database of 82 radar stations and more than 1500 rain gauges in continental USA was compiled and used for the continuous downloading of radar images and rain data. Image sequences corresponding to rain events were extracted for two randomly selected radar stations in South and North Carolina. Rainfall data from multiple gauges within the radar zone of 124 nautical miles (nm) (∼230 km) were extracted and combined with corresponding reflectivity values for each time interval of the selected rain event. Data were normalised to one‐hour intervals and then statistical analysis was applied to study the potential correlation. Results of regression analysis showed a significant correlation between rain gauge data and radar reflectivity values and allowed derivation of empirical formulae. Copyright © 2005 Royal Meteorological Society.

Entropy-based assessment and clustering of potential water resources availability

The potential water resources availability (PWRA) in an area is assessed in terms of disorder in intensity and over-a-year apportionment of monthly rainfall. The disorder is measured by two different entropies, intensity entropy ( IE) and apportionment entropy ( AE), which are defined using Shannon's informational entropy theory. These entropies are standardized and pairs of standardized IE and AE for different locations of raingauges are plotted. Then, simple clustering and K-means clustering are considered for delineating PWRA distributed over an area of interest and classifying regional attributes of PWRA. Monthly rainfall data recorded at 11,260 raingauges are analyzed, and different classifications of PWRA are comparatively mapped. An example application of the proposed methodology to the worldwide PWRA assessment is discussed.

Appropriate Spatial Sampling of Rainfall or Flow Simulation/Echantillonnage Spatial de la Pluie Appropri� pour la Simulation D'�coulements

Abstract The objective of this study is to find the appropriate number and location of raingauges for a river basin for flow simulation by using statistical analyses and hydrological modelling. First, a statistical method is used to identify the appropriate number of raingauges. Herein the effect of the number of raingauges on the cross-correlation coefficient between areally averaged rainfall and discharge is investigated. Second, a lumped HBV model is used to investigate the effect of the number of raingauges on hydrological modelling performance. The Qingjiang River basin with 26 raingauges in China is used for a case study. The results show that both cross-correlation coefficient and modelling performance increase hyperbolically, and level off after five raingauges (therefore identified to be the appropriate number of rain-gauges) for this basin. The geographical locations of raingauges which give the best and worst hydrological modelling performance are identified, which shows that there is a strong dependence on the local geographical and climatic patterns.

GEOSTATISTICAL METHODS FOR PREDICTION OF SPATIAL VARIABILITY OF RAINFALL IN A MOUNTAINOUS REGION

Reliable estimation of rainfall distribution in mountainous regions poses a great challenge not only due to highlyundulating surface terrain and complex relationships between land elevation and precipitation, but also due tonon-availability of abundant rainfall measurement points. Prediction of rainfall variability over mountainous islands is alogical step towards meaningful land use planning and water resources zoning. In this context, geostatistical techniques weredeveloped for mapping the rainfall variability over the island of St. Lucia in the Caribbean, using the elevation informationextracted from a Digital Elevation Model (DEM) and long-term mean monthly rainfall (MMR) data of 40 raingauge stationsspread over 616 km2. The ordinary co-kriging (OCK) and collocated co-kriging (CCK) methods of interpolation were appliedfor the standardized rainfall depths associated with elevation, as the primary variate, and the surface elevation values as thesecondary variate. The best semivariogram model algorithm generated, using either of the above co-kriging (CK) methods,was used to predict standardized values for the elevation points extracted from the DEM for which the rainfall depths werenot known. The predicted values were further destandardized to generate the rainfall depth at the unmeasured locations.Ordinary kriging (OK) was then performed for the destandardized and observed rainfall depths to generate the predictionmap of MMR over the entire island. These sequential steps were repeated for the MMR data of all twelve months to generaterainfall prediction maps over the island. The spherical semivariogram model fit well (0.84 < R2 < 0.98) for both the OCKand OK methods. The cross-validation error statistics of OCK presented in terms of coefficient of determination (R2), krigedroot mean square error (KRMSE), and kriged average error (KAE) were within the acceptable limits (KAE close to zero, R2close to one, and KRMSE from 0.55 to 1.45 for 40 raingauge locations) for most of the months. The exploratory data analysis,variogram model fitting, and generation of MMR prediction map through kriging were accomplished through use of ArcGISand GS+ software.

Comparison of two approaches for downscaling synoptic atmospheric patterns to multisite precipitation occurrence

The physical linkages between climate on the large scale and weather on the local scale allow the formulation of downscaling approaches for assessing the impact of climate variability at point locations. This paper presents a comparison between two such approaches applied for downscaling synoptic atmospheric patterns to point rainfall occurrences on a rain gauge network. The approaches evaluated are the parametric nonhomogenous hidden Markov model (NHMM) and the nonparametric k‐nearest neighbor downscaling approach. The NHMM defines local‐scale weather as a function of a discrete weather state that is Markovian and depends on predictor variables representing synoptic atmospheric patterns. As the model is defined parametrically, the number of parameters that need specification increases as one considers more discrete weather states. Consequently, parameter identification and generalization to ungauged sites becomes difficult. On the other hand, nonparametric resampling is attractive because of its efficiency and simplicity, being structured as a direct probabilistic relationship between the larger‐scale climatic variables and the local‐scale weather. Such a formulation offers a simpler alternative to the NHMM approach of using intermediate hidden weather state variables but is less capable of representing persistence introduced through Markovian assumptions in the NHMM. In the comparison presented here, we applied weather‐state‐based nonhomogeneous hidden Markov model and the k‐nearest neighbor bootstrap to estimate precipitation occurrences at a network of 30 rain gauge locations around Sydney, Australia. Our results suggest that both the models perform well in representing spatial variations while they show a lack in representing temporal dependence at scales longer than a few days as exhibited through wet spell length characteristics. Local‐scale features that are difficult to represent through the large‐scale climate predictors are, as expected, not reproduced by either approach.

Improving Operational Measurement of Precipitation Using Radar in Mountainous Terrain—Part I: Methods

Four major rainfall events that affected the Western Alps between autumn 1994 and autumn 2000 are analyzed to assess the bias between the radar rainfall estimates at rain gauge locations and the gauge amounts. The aim of this study is to demonstrate the importance of: 1) bias adjustment; 2) the training procedure used to train various adjustment methods by means of independent data; and 3) a quality check of the radar-gauge couples that were used for the training itself. A first adjustment method is simply based on a single "bias-correction" coefficient. A weighted multiple regression (WMR) is well worth the additional effort of determining three additional coefficients, which give a spatial distribution of the adjustment factor rather than a constant one for the whole domain as the output. The independent dataset that was used to train the gauge-adjustment techniques consists of daily radar/gauge amounts accumulated during the first day of each event. The following days are used for an independent verification that is dealt with in a companion letter, which will validate the methods and illustrate the improvements and the feasibility of a real-time application during intense events. The WMR technique tries to correct not only the overall bias but also the beam-broadening, visibility, and orography influences. The training procedure of both the bulk- and WMR-adjustment methods highlighted a considerable radar underestimation, which is certainly not surprising in mountainous terrain. The WMR-derived coefficients also clearly show that the radar underestimates precipitation for higher sampling volumes and longer distances. Since the WMR is fast and simple to use, it represents an alternative to more sophisticated methods and seems to be particularly useful for operational services.

Rainfall climate limitation to slurry spreading in Ireland

Animal production systems in Ireland are generally based on rotational grass grazing, grass forage conservation, winter housing and indoor winter feeding because of the humid, temperate climate. These systems give rise to the need for slurry storage and land spreading. Slurry spreading in winter can cause pollution due to excessive runoff when soils have a water content above field capacity. A method of determining the rainfall climate limitation to safe slurry spreading is presented for Ireland. The method examines rain gauge records using a filter of set duration, magnitude and shape. If a sequence of days all have less rainfall than the defined filter then a safe spreading day is allotted. Using this approach it is possible to estimate the probability of safe spreading periods arising on an annual basis. Several filter types were tested using 10 rain gauge locations to find the most suitable filter characteristics. A somewhat conservative, off-set V-shaped filter of 8 days duration was then used to evaluate 151 rain gauge records throughout Ireland and produce a map of the probability of safe spreading periods having occurred within the rainfall climate record. Such a map, once combined with other regional scale soil and environmental information could be used for developing national strategies for planning of, and data acquisition for, site-specific, regulated, safe slurry spreading.

Use of dual‐frequency microwave links for measuring path‐averaged rainfall

Results are presented from an experiment designed to test the use of dual‐frequency microwave attenuation for measuring path‐averaged rainfall. In general, the relation between attenuation and path‐averaged rainfall is nonlinear. However, for carefully chosen pairs of frequencies, the relation between the difference in attenuations and path‐averaged rainfall is well represented by a straight line through the origin. The slope of this line is essentially unaffected by variations in the (unknown) drop size distribution, drop shape, and drop temperature. This paper describes the results obtained from a pair of links operating at 12.8 GHz and 17.6 GHz on a 23.3 km path. Results are shown for three individual events, including comparisons with rainfall estimates obtained from more traditional sources: a local rain gauge network and an adjacent weather radar. We report summary results for 112 events recorded on this path during almost 2 years of observation, and also for 52 events recorded on a nearby 13.9 km path operating at 13.9 GHz and 22.9 GHz. In both cases the results suggest that dual‐frequency links could serve as useful additional tools for the measurement of rainfall, particularly in locations such as urban areas and obscured mountain valleys, where traditional measuring instruments struggle to provide accurate estimates.

An adaptive neuro-fuzzy inference system for the post-calibration of weather radar rainfall estimation

An Adaptive Neuro-Fuzzy Inference System, based on a jack-knife approach, is proposed for the post-calibration of weather radar rainfall estimation exploiting available raingauge observations. The methodology relies on the construction of a fuzzy inference system with three inputs (radar x coordinate, y coordinate and rainfall estimation at raingauge locations) and one output (raingauge observations). Subtractive clustering is used to generate the initial fuzzy inference system. Artificial neural network learning provides a fast way to automatically generate additional fuzzy rules and membership functions for the fuzzy inference system. Fuzzy logic enhances the generalisation of the artificial neural network system. In order to demonstrate the steps of the radar rainfall post-calibration using the Adaptive Neuro-Fuzzy Inference System, CAPPIs of one-hour rainfall accumulation and corresponding raingauge observations have been selected. Results show that the proposed approach looks for a response that is a compromise between radar rainfall estimations and raingauge observations and does not necessarily consider the raingauge observations as ground truth. The algorithm is very fast and can be implemented for real time post-calibration. This algorithm makes use of all available data—raingauge observations are usually scarce—for training and checking the neuro-fuzzy inference system. It also provides a degree of reliability of the post-calibration.

Temporal and spatial rainfall analysis across a humid tropical catchment

AbstractTemporal and spatial rainfall patterns were analysed to describe the distribution of daily rainfall across a medium‐sized (379km2) tropical catchment. Investigations were carried out to assess whether a climatological variogram model was appropriate for mapping rainfall taking into consideration the changing rainfall characteristics through the wet season. Exploratory, frequency and moving average analyses of 30 years' daily precipitation data were used to describe the reliability and structure of the rainfall regime. Four phases in the wet season were distinguished, with the peak period (mid‐August to mid‐September) representing the wettest period. A low‐cost rain gauge network of 36 plastic gauges with overflow reservoirs was installed and monitored to obtain spatially distributed rainfall data. Geostatistical techniques were used to develop global and wet season phase climatological variograms. The unscaled climatological variograms were cross‐validated and compared using a range of rainfall events. Ordinary Kriging was used as the interpolation method. The global climatological variogram $\gamma^{*} = 1{\cdot}2 \left[1 - \exp - {h\over 18 \hbox{ km}}\right]$ performed better, and was used to optimize the number and location of rain gauges in the network. The research showed that although distinct wet season phases could be established based on the temporal analysis of daily rainfall characteristics, the interpolation of daily rainfall across a medium‐sized catchment based on spatial analysis was better served by using the global rather than the wet season phase climatological variogram model. Copyright © 2001 John Wiley &amp; Sons, Ltd.

Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

Thirty major storms that passed over Goodwin Creek, a small research watershed in northern Mississippi, were analyzed to assess the bias between radar rainfall estimates at rain gauge locations and the gauge amounts. These storms, each contributing at least 10 mm of storm total rainfall, accumulated approximately 785 mm of rain, which corresponds to about half the average annual rainfall amount for the area. The focus of this study was to demonstrate the importance of (1) bias adjustment of the radar rainfall estimates and (2) the quality control of the rain gauge data used for bias adjustment. The analyses are based on Memphis Weather Surveillance Radar—1988 Doppler radar data, tipping‐bucket rain gauge data, and raindrop spectra information collected within the Goodwin Creek catchment. Because of measurement and rainfall estimation uncertainties, radar observations are often combined with rain gauge data to obtain the most accurate rainfall estimates. Rain gauge data, however, are subject to characteristic error sources: for Goodwin Creek, malfunctioning of the tipping‐bucket rain gauges was frequently caused by biological and mechanical fouling, and human interference. Therefore careful quality control of the rain gauge data is crucial, and only good quality rain gauge information should be used for adjusting radar rainfall estimates. By using high‐quality gauge data and storm‐based bias adjustment, we achieved radar rainfall estimates with root‐mean‐square errors (RMSE) of approximately 10% for storm total rainfall accumulations of 30 mm or more. Differences resulting from radar data processing scenarios were found to be small compared to the effect caused by bias adjustment and using high‐quality rain gauge data.