Automatic Precipitation Measurement Based on Raindrop Imaging and Artificial Intelligence

Rainfall measurement is subjected to various uncertainties due to the complexity of measurement techniques and atmosphere characteristics associated with weather type. Thus, this article presents a video-based disdrometer to analyze raindrop images by introducing artificial intelligence technology for the rainfall rate. First, a high-speed CMOS camera is integrated into a planar LED as a backlight source for appropriately acquiring falling raindrops in different positions. The falling raindrops can be illuminated and used for further image analysis. Algorithms developed for raindrop detection and trajectory identification are employed. In a field test, a rainfall event of 42 continuous hours has been measured by the proposed disdrometer that is validated against a commercial PARSIVEL² disdrometer and a tipping bucket rain gauge at the same area. In the evaluation for 5-min rainfall images, the results of the trajectory identification are within the precision of 87.8%, recall of 98.4%, and F1 score of 92.8%, respectively. Furthermore, the performance exhibits that the rainfall rate and raindrop size distribution (RSD) obtained by the proposed disdrometer are remarkably consistent with those of PARSIVEL² disdrometer. The results suggest that the proposed disdrometer based on the continuous movements of the falling raindrops can achieve accurate measurements and eliminate the potential errors effectively in the real-time monitoring of rainfall

Polarimetric weather radar retrieval of raindrop size distribution by means of a regularized artificial neural network

The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dual-polarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift value

Runoff, discharge and flood occurrence in a poorly gauged tropical basin : the Mahakam River, Kalimantan

Tidal rivers and lowland wetlands present a transition region where the interests of hydrologists and physical oceanographers overlap. Physical oceanographers tend to simplify river hydrology, by often assuming a constant river discharge when studying estuarine dynamics. Hydrologists, in turn, generally ignore the direct or indirect effects of tides in water level and discharge records. This thesis aims to improve methods to monitor, model and predict discharge dynamics in tidal rivers and lowland wetlands, by focussing on the central and lower reaches of the River Mahakam (East Kalimantan, Indonesia), and the surrounding lakes area. The 980-km long river drains an area of about 77100 km2 between 2°N - 1°S and 113°E - 118°E. Due to its very mild bottom slope, a significant tidal influence occurs in this river. The middle reach of the river is located in a subsiding basin, parts of which are below mean sealevel, featuring peat swamps and about thirty lakes connected to the river via tie channels. Upstream of the lakes area, at about 300 km from the river mouth, an acoustic Doppler current profiler (H-ADCP) has been horizontally deployed at a station near the city of Melak (Chapter 2). The H-ADCP profiles of velocity are converted to discharge adopting a new calibration methodology. The obtained time-series of discharge show the tidal signal is clearly visible during low flow conditions. Besides tidal signatures, the discharge series show influences by variable backwater effects from the lakes, tributaries and floodplain ponds. The discharge rate at the station exceeds 3250 m3s-1 with a hysteretic behaviour. For a specific river stage, the discharge range can be as high as 2000 m3s-1. Analysis of alternative types of rating curves shows this is far beyond what can be explained from kinematic wave dynamics. Apart from backwater effects, the large variation of discharge for a specified river stage can be explained by river-tide interaction, impacting discharge variation especially in the fortnightly frequency band. A second H-ADCP station has been setup in the lower reach of the Mahakam, near the city of Samarinda, where the tidal discharge amplitude generally exceeds the discharge related to runoff (Chapter 3). Conventional rating curve techniques are inappropriate to model river discharge at this tidally influenced station. As an alternative, an artificial neural network (ANN) model is developed to investigate the degree to which tidal river discharge at Samarinda station can be predicted from an array of level gauge measurements along the tidal river, and from tidal level predictions at sea. The ANN-based model produces a good discharge estimation, as established from a consistent performance during both the training and the validation periods, showing the discharges can be predicted from water levels only, once that a trained model is available. The ANN models perform well in predicting discharges up to two days in advance. Chapter 4 addresses the role of backwater effects and tidal influences on discharge time-series used to calibrate a rainfall-runoff model. The HBV rainfall-runoff model is implemented for the Mahakam sub-catchment upstream of Melak (25700 km2). In a first approach, the model is calibrated using a discharge series derived from the H-ADCP measurements from Melak station. In a second approach, discharge estimates derived from a rating curve are used to calibrate the model. Adopting the first approach, a comparatively low model efficiency is obtained, which is attributed to the backwater and tidal effects that are not captured in the model. The second approach produces a relatively higher model efficiency, since the rating curve filters the backwater effects out of the discharge series. Seasonal variation of terms in the water balance is not affected by the choice for one of the two calibration strategies, which shows that backwaters do not have a systematic seasonal effect on the river discharge. To allow for investigation of the causes of backwater effects, satellite radar remote sensing is employed to monitor water levels in wetlands (Chapter 5). A series of Phased Array L-band Synthetic Aperture Radar (PALSAR) images is used to observe the dynamics of the Mahakam River floodplain. To analyze radar backscatter behavior for different land cover types, several regions of interest are selected, based on land cover classes. Medium shrub, high shrub, fern/grass, and degraded forest are found to be sensitive to flooding, whereas peat forest, riverine forest and tree plantation backscatter signatures only slightly change with flood inundation. An analysis of the relationship between radar backscatter and water levels is carried out. For lakes and shrub covered peatland, for which the range of water level variation is high, a good water level-backscatter correlation is obtained. In peat forest covered peatland, subject to a small range of water level variation, water level-backscatter correlations are poor, limiting the ability to obtain a floodplain-wide water surface topography from the radar images. Chapter 6 continues to investigate the degree in which satellite radar remote sensing can serve to distinguish between dry areas and wetlands, which is a difficult task in densely vegetated areas such as peat domes. Flood extent and flood occurrence information are successfully extracted from a series of radar images of the middle Mahakam lowland area. A fully inundated region is easily recognized from a dark signature on radar images. Open water flood occurrence is mapped using a threshold value taken from radar backscatter of the permanently inundated areas. Radar backscatter intensity analysis of the vegetated floodplain area reveals consistently higher backscatter values, indicating flood inundation under forest canopy. Those observations are used to establish thresholds for flood occurrence mapping in the vegetated area. An all-encompassing flood occurrence map is obtained by combining the flood occurrence maps for areas with and without vegetation. Chapter 7 synthesizes the findings from the previous chapters. It is concluded that the backwater effects and subtle tidal influences may prevent the option to predict river discharge using rating curves, which can best be interpreted as a stage-runoff relationship. H-ADCPs offer a promising alternative to monitor river discharge. For a tidal river, an ANN model can be used as a tool for data gap filling in an H-ADCP based discharge series, or even to derive discharge estimates solely from water levels and water level predictions. Discharge can be predicted several time-steps ahead, allowing water managers to take measures based on forecasts. The stage-runoff relationship derived from a continuous series of H-ADCP based discharge estimates may be expected to be much more accurate than a similar rating curve derived from a small number of boat surveys. The flood occurrence map derived from PALSAR images can offer a detailed insight into the hydroperiod, the period in which a soil area is waterlogged, and flood extent of the lowland area, illustrating the added value of radar remote sensing to wetland hydrological studies. In future work, radar-based floodplain observations may serve to calibrate hydrodynamic models simulating the processes of flooding and emptying of the lakes area.</p

Variability of the raindrop size distribution across scales in Mediterranean rainfall:characterisation and stochastic simulation

Measurement of rain is made difficult by the high variability of the precipitation process, down to raindrop scale. Point measurements are generally accurate, but their lack of spatial representativeness is a significant limitation. Weather radars indirectly measure rainfall over large regions, but the microphysical properties of the rain being measured must be known or inferred in order to compute rainfall quantities from radar data. The raindrop size distribution (DSD) statistically describes the microstructure of rain. While the DSD is often assumed to be uniform in space, it is in fact highly variable. The work in this thesis contributes to the understanding of the small-scale variability of the DSD and its effects on the measurement of rainfall. The methods shown were developed using data from a network of disdrometers and radars over a 13 x 7 km2 field site in Ardeche, France. This area experiences heavy Mediterranean rainfall. A technique to improve the accuracy of DSD measurements made by Parsivel disdrometers is proposed. The method uses a 2D-video-disdrometer as a reference instrument. A new geostatistical method for spatial interpolation and stochastic simulation of the experimental DSD is provided. It can estimate or simulate the non-parametric DSD at an unmeasured location, conditional on nearby measurements. Leave-one-out testing showed that estimates were produced with minimal bias. The correction and simulation techniques were used together to investigate the small-scale variability of the DSD in the study region. DSD variability was studied in detail over two typical scales, corresponding to the footprint size of the Global Precipitation Mission (GPM) space-borne weather radar, and a typical size for an operational numerical weather model pixel. It is shown that the assumption that a point measurement of the DSD represents an areal estimation introduces error that increases with areal size and drop size. Satellite and weather model rainfall retrieval algorithms that correspond to these two typical domains were tested, and while it was found that rain intensity and radar reflectivity were well retrieved, other DSD properties were often not representative of the sub-grid process. Double-moment normalisation provides a compact representation of the DSD, under the assumption that the normalised version DSD is invariant. This assumption was tested using instrument network data in France, Switzerland, and the United States. It is shown in this work that for practical purposes, the double-normalised DSD can be assumed invariant through horizontal and vertical displacement. Using this assumption, a new method for retrieval of the DSD from polarimetric radar data is proposed. The new DSD-retrieval technique performs as well or better than an existing method. An application of multifractal analysis to high-resolution snowfall data from the Swiss Alps is presented. Scaling of snowfall was observed in reconstructed vertical columns, at scales from about 35 metres to two metres, with no scaling observed at smaller scales. Weak scaling was observed in time series. The results indicate that at small (sub-metre or sub-minute) scale, snowfall appears homogeneously distributed