Miguel A. Rico-Ramirez

Application of ANN and ANFIS models for reconstructing missing flow data

Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.

Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available.

Rainfall Estimation with an Operational Polarimetric C-Band Radar in the United Kingdom: Comparison with a Gauge Network and Error Analysis

AbstractThe estimate of rainfall using data from an operational dual-polarized C-band radar in convective storms in southeast United Kingdom is compared against a network of gauges. Four different rainfall estimators are considered: reflectivity–rain-rate (Z–R) relation, with and without correcting for rain attenuation; a composite estimator, based on (i) Z–R, (ii) R(Z, Zdr), and (iii) R(Kdp); and exclusively R(Kdp). The various radar rain-rate estimators are developed using Joss disdrometer data from Chilbolton, United Kingdom. Hourly accumulations over radar pixels centered on the gauge locations are compared, with approximately 2500 samples available for gauge hourly accumulations > 0.2 mm. Overall, the composite estimator performed the “best” based on robust statistical measures such as mean absolute error, the Nash–Sutcliffe coefficient, and mean bias, at all rainfall thresholds (>0.2, 1, 3, or 6 mm) with improving measures at the higher thresholds of >3 and >6 mm (higher rain rates). Error variance ...

Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH Land surface model

Microwave remote sensing and mesoscale weather models have high potential to monitor global hydrological processes. The latest satellite soil moisture dedicated mission SMOS and WRF-NOAH Land Surface Model (WRF-NOAH LSM) provide a flow of coarse resolution soil moisture data, which may be useful data sources for hydrological applications. In this study, four data fusion techniques: Linear Weighted Algorithm (LWA), Multiple Linear Regression (MLR), Kalman Filter (KF) and Artificial Neural Network (ANN) are evaluated for Soil Moisture Deficit (SMD) estimation using the SMOS and WRF-NOAH LSM derived soil moisture. The first method (and most simplest) utilizes a series of simple combinations between SMOS and WRF-NOAH LSM soil moisture products, while the second uses a predictor equation generally formed by dependent variables (Probability Distributed Model based SMD) and independent predictors (SMOS and WRF-NOAH LSM). The third and fourth techniques are based on rigorous calibration and validation and need proper optimisation for the final outputs backboned by strong non-linear statistical analysis. The performances of all the techniques are validated against the probability distributed model based soil moisture deficit as benchmark; estimated using the ground based observed datasets. The observed high Nash Sutcliffe Efficiencies between the fused datasets with Probability Distribution Model clearly demonstrate an improved performance from the individual products. However, the overall analysis indicates a higher capability of ANN and KF for data fusion than the LWA or MLR approach. These techniques serve as one of the first demonstrations that there is hydrological relevant information in the coarse resolution SMOS satellite and WRF-NOAH LSM data, which could be used for hydrological applications.

Bright-band detection from radar vertical reflectivity profiles

The use of quantitative scanning weather radar for precipitation measurements is a vital element of modern hydrology and limits the development of all distributed models of catchment behaviour. The presence of the so‐called bright band (or melting layer) contaminates the quantitative precipitation estimates and has delayed the widespread take‐up of radar‐based precipitation estimates in operational models. The study of the Vertical Reflectivity Profile (VRP) of precipitation is important in order to develop algorithms to correct scanning weather radar measurements for the variation of the VRP at long ranges. Therefore, this paper presents an algorithm to detect the extent of the bright band using high‐resolution VRPs. The boundaries of the bright band are identified by a new algorithm which utilizes a rotational coordinate system for identifying the upper and lower parts of the bright band. This overcomes some of the difficulties experienced when using the gradient of the reflectivity in conventional bright‐band detection algorithms. The reflectivities above, within, and below the bright band are then used to construct idealized VRPs to correct scanning weather radar measurements.

Non-parametric rain/no rain screening method for satellite-borne passive microwave radiometers at 19–85 GHz channels with the Random Forests algorithm

This paper presents a novel non-parametric pattern recognition method to screen rain/no rain status for satellite-borne passive microwave radiometers in the 19–85 GHz channels. The method is based on randomized decision trees with bootstrap aggregation (Random Forests (RF) algorithm). It relies on pragmatic associations between the input features using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) calibrated brightness temperatures and precipitation radar (PR) rain/no rain information as targets. Both these instruments are carried on board the TRMM satellite. In order to develop the method, first, the 10 most significant input features are selected by using feature importance criteria through out-of-bag (OOB) statistics from a total of 17 input features. The input features include the brightness temperatures, as well as some computed signatures – polarization differences (PD), polarization-corrected temperatures (PCT), and scattering indices (SI) at in the 19–85 GHz channels. The feature selection is carried out for different types of surface terrain (ocean, land, and coast), and the selected features are then used for final RF algorithm development. During the dichotomous statistical assessment of the method against the PR rain/no rain status as ‘truth’, the presented method produced reasonable threat scores of 0.50, 0.43, and 0.39, respectively, over ocean, land, and coast surface terrains. Furthermore, the results are compared with the dichotomous scores derived by the Goddard profiling algorithm (GPROF) and, remarkably, the RF-based method corroborated better statistical scores than that of the GPROF. The presented method does not rely on any a priori information and is applicable to other passive microwave radiometers at similar frequencies.

Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area

Abstract Due to the relatively small spatial scale, as well as rapid response, of urban drainage systems, the use of quantitative rainfall forecasts for providing quantitative flow and depth predictions is a challenging task. Such predictions are important when consideration is given to urban pluvial flooding and receiving water quality, and it is worthwhile to investigate the potential for improved forecasting. In this study, three quantitative precipitation forecast methods of increasing complexity were compared and used to create quantitative forecasts of sewer flows 0–3 h ahead in the centre of a small town in the north of England. The HyRaTrac radar nowcast model was employed, as well as two different versions of the more complex STEPS model. The STEPS model was used as a deterministic nowcasting system, and was also blended with the Numerical Weather Prediction (NWP) model MM5 to investigate the potential of increasing forecast lead-times (LTs) using high-resolution NWP. Predictive LTs between 15 and 90 min gave acceptable results, but were a function of the event type. It was concluded that higher resolution rainfall estimation as well as nowcasts are needed for prediction of both local pluvial flooding and combined sewer overflow spill events. Editor D. Koutsoyiannis; Guest editor R.J. Moore Citation Schellart, A., Liguori, S., Krämer, S., Saul, A., and Rico-Ramirez, M.A., 2014. Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area. Hydrological Sciences Journal, 59 (7), 1418–1436. http://dx.doi.org/10.1080/02626667.2014.920505

Classification of Weather Radar Images using Linguistic Decision Trees with Conditional Labelling

This paper focuses on the application of LID3 (linguistic decision tree induction algorithm) to the classification of weather radar images. In radar analysis a phenomenon known as bright band occurs. This essentially is an amplification in reflectivity due to melted snow and leads to overestimation of precipitation. It is therefore beneficial to detect this bright band region and apply the appropriate corrections. This paper uses LID3 in order to identify the bright band region pixel by pixel in real time. This is not possible with the current differencing methods currently used for bright band detection. LID3 also allows us to infer a set of linguistic rules to further our understanding of the relationship between radar measurements and the classification of bright band. A new idea called conditional labeling is proposed, which attempts to ensure a more efficiently partitioned space, omitting relatively sparse branches caused by attribute dependencies.

The impact of raindrop drift in a three-dimensional wind field on a radar–gauge rainfall comparison

There are many causes for the discrepancies between weather radar and rain gauges, and among these, displacement of raindrops due to wind drift – which is especially a problem with high-spatial resolution weather radar – is largely ignored in the published literature. This is mainly due to the lack of high-resolution three-dimensional wind fields and feasible treatment of the raindrop size distribution (DSD). In this study, a new systematic approach is proposed to explore the radar–gauge relationship under the wind influence. The mass-weighted mean diameter of raindrops is derived for each radar grid from the DSD data. The reanalysis project ERA-40 data of the European Centre for Medium-range Weather Forecasts (ECMWF) are used to drive the numerical weather research and forecasting (WRF) model to generate high-resolution hourly three-dimensional wind fields. Trajectories and displacements of raindrops are then computed using a three-dimensional motion equation from the given radar beam height to the ground surface. Based on the radar rainfall surface interpolated by the bicubic spline method, the correlation of the radar–gauge pairs is used to validate the results. A case study with 20 storm events in the Brue catchment in South West England is chosen to evaluate the proposed scheme. It has been found that when wind drift is taken into account, the correlation coefficient in hourly gauge–radar comparisons can be enhanced by up to 30% and the average correlation coefficient for an event can be improved by 10%. However, there are still some situations in which the scheme fails to work, indicating the complexity and uncertainties in tackling this challenging problem. Further studies are needed to explore why those cases cause problems to the scheme and how it could be improved to cope with them.

Calibration of Roughness Parameters Using Rainfall–Runoff Water Balance for Satellite Soil Moisture Retrieval

AbstractSoil moisture is an important component in hydrologic and meteorologic processes. Remote sensing devices, such as satellite radiometers, are useful tools to obtain soil moisture information over a large region. However, effective “ground truth” calibration data for satellite sensors are lacking. This paper presents a new approach on the basis of rainfall and river runoff hydrologic data to estimate satellite-based soil moisture. The catchment water storage change from the rainfall and river runoff has a strong link with the soil moisture information and the parameterization of the surface roughness parameters h and Q. The proposed methodology is tested at the Brue catchment in southwest England. Two years of satellite data are used for calibrating and retrieving surface soil moisture, and 1 year of data is used for validation. This study indicates that the estimated daily soil moisture from satellite correlates well with the flow observations after applying the new calibration method, and good agr...