Radar Rain Gauge Research Articles

Rainfall-induced Gravity Movement of the Unzen-Fugendake Volcanic Dome Analysis combining Ground-Radar Interferometry and XRAIN Rainfall radar system

Lava domes created by volcanoes often cause pyroclastic and debris flows, which have a significant impact on the surrounding infrastructure and population and have been the subject of much research. However, because volcanic domes tend not to survive the eruptions that form them, the instability of domes that survive eruptions such as Unzen Fugendake in Japan is both a poorly understood process and a danger. Therefore, the present contribution aims to (1) Displacement and precipitation from 2018 to 2020 for lava domes at Unzen volcano and their relationship to earthquakes, and (2) Haar wavelet analysis to understand the response of displacement to precipitation. The method is based on dome displacement from the Unzen Ground Based Synthetic Aperture Radar system and 48-hour rainfall from the MP radar rain gauge system. As a result, the authors confirmed the following: (1) precipitation of 150 mm or more in 48 hours tends to increase the vertical fluctuation of the dome, and even rainfall of less than 100 mm per 48 hours has a similar effect when it is repeated in an intensive manner; (2) After precipitation, major dome displacement can take days or weeks to occur, and is not instantaneous like the dome collapses in Soufriere and Merapi.

Measuring annual precipitation with a radar rain gauge in severe mountain conditions

Cílem článku je popis zkušeností s využitím alternativní technologie měření celoročních srážek v náročných horských podmínkách bez zdroje elektrické energie. Za tímto účelem byl v roce 2020 na Šumavě v nadmořské výšce 1 270 m n. m. instalován radarový srážkoměr WS100 od firmy Lufft. Z dosavadního měření lze pozitivně hodnotit zejména bezúdržbovost senzoru, podrobný krok měření a například rozlišování typu srážek. Otázkou zůstává přesnost měření, kdy při některých srážkových epizodách srážkoměr pravděpodobně svá měření nadhodnocuje. Přesné srovnání s jiným měřením je v těchto hřebenových podmínkách obtížné. Zároveň však senzor produkuje i přesná měření, která lze ověřit vedle umístěným nevyhřívaným člunkovým srážkoměrem a měřením výšky sněhu. Tímto způsobem byla vyloučena systematická chyba. Měření bude nadále pokračovat za účelem podrobnějšího hodnocení. Senzor je mimo jiné součástí monitoringu povodí Kaplického potoka v Národní přírodní rezervaci (NPR) Boubín, kde je sledován také odtok. Z tohoto hlediska jsou informace o srážkách a jejich typu důležité pro vyhodnocení hydrologických vlastností povodí.

Peak discharges per unit area increase with catchment area in a high-relief mountains with permeable sedimentary bedrock

To improve the accuracy of flood-flow prediction under changing climate conditions, understanding the storm-flow generation at various scales in a catchment is essential. Flood-flow prediction is conducted assuming that peak discharge per unit area (hereafter referred to as peak unit discharge) decreases with catchment area, based on observations. However, this assumption may not be applicable in steep high-relief mountain catchments with permeable bedrock, although this has not been confirmed due to the difficulty of flood-flow measurement in high-relief mountains. We obtained continuous discharge data for three nested catchments with areas of 0.58 km2, 2.2 km2 and 94 km2 in the high-relief Chichibu Mountains, which have permeable bedrock, and tested the hypothesis that peak unit discharge and direct runoff; flow caused by and directly following a rainfall and forms the major part of the flood hydrograph excluding base flow per unit area (hereafter referred to as unit direct runoff) were greater in larger catchments. The effect of the spatial distribution of rainfall on the spatial distribution of storm runoff was evaluated based on radar-rain gauge analyzed precipitation data after verification using ground-based rainfall data. We obtained data for 67 storms, with a depth of rainfall-event ranging from 26.5 to 231.5 mm and a return period ranging from 10-3 to 3.2 y. During significant floods, with peak unit discharge at largest catchment was greater than 3 mm/h; peak unit discharge and unit direct runoff were always larger at larger catchment. Differences in peak unit discharge and unit direct runoff among measured catchments were often several times and sometimes more than an order of magnitude, depended on the storm events. While, the catchment mean depth of rainfall-events was up to 1.26 times greater in the largest catchment, relative to the two smaller catchments, implying that spatial variations in storm rainfall have little effect on the differences in flood flow among catchments. Our study and previous research indicate that this increasing storm runoff per unit area with catchment area is characteristic of catchments with permeable bedrock, where some rainfall that infiltrates bedrock on hillslopes is transported downstream within the bedrock to create storm flow in larger catchments.

The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China

Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.

Utilization of Weather Radar Data for the Flash Flood Indicator Application in the Czech Republic

In the past few years, demands on flash flood forecasting have grown. The Flash Flood Indicator (FFI) is a system used at the Czech Hydrometeorological Institute for the evaluation of the risk of possible occurrence of flash floods over the whole Czech Republic. The FFI calculation is based on the current soil saturation, the physical-geographical characteristics of every considered area, and radar-based quantitative precipitation estimates (QPEs) and forecasts (QPFs). For higher reliability of the flash flood risk assessment, calculations of QPEs and QPFs are crucial, particularly when very high intensities of rainfall are reached or expected. QPEs and QPFs entering the FFI computations are the products of the Czech Weather Radar Network. The QPF is based on the COTREC extrapolation method. The radar-rain gauge-combining method MERGE2 is used to improve radar-only QPEs and QPFs. It generates a combined radar-rain gauge QPE based on the kriging with an external drift algorithm, and, also, an adjustment coefficient applicable to radar-only QPEs and QPFs. The adjustment coefficient is applied in situations when corresponding rain gauge measurements are not yet available. A new adjustment coefficient scheme was developed and tested to improve the performance of adjusted radar QPEs and QPFs in the FFI.

Evaluation of Integrated Nowcasting through Comprehensive Analysis (INCA) precipitation analysis using a dense rain-gauge network in southeastern Austria

Abstract. An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps in decision making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeastern Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain-gauge network with about one station per 2 km2 – as “true precipitation”. We analyze annual, seasonal, and extreme precipitation of the 1 km × 1 km INCA product and its development from 2007 to 2018. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (“wet season”). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. Since new radars were installed during this period, we conclude that this increase in the overestimation is due to new radars' systematic errors. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1 h minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results show that the INCA analysis product has been improving; nevertheless, the errors and uncertainties of INCA to estimate short-duration convective rainfall events and the peak of extreme events should be considered for future studies. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in regions with moderately hilly topography and convective dominance in summer.

Merging radar and rain gauge data by using spatial–temporal local weighted linear regression kriging for quantitative precipitation estimation

Merging radar and rain gauge data is an important means of obtaining precipitation products with high accuracy and high spatial–temporal resolution. However, due to the change of the raindrop size distribution itself and the uncertainty of the radar observational data, it is an indisputable fact that A and b in the Z–R (radar reflectivity factor (Z) to rainfall rate (R)) relationship are complex functions of space and time, which has a great influence on the accuracy of radar–rain gauge quantitative precipitation estimation (QPE). In this study, in an effort to further improve the accuracy of radar–rain gauge QPE, spatial–temporal local weighted linear regression (STLWLR) and its corresponding regression kriging (STLWLRK) are proposed for merging radar and rain gauge data for QPE, for which the maximum number of Z–R pairs in the spatial–temporal neighborhood N, the spatial–temporal distance transformation parameter μ, and the specified threshold C of |e/σ| are three key parameters. Specifically, cases that occurred on Hainan Island and the cross-validation mode were used to calibrate the three key parameters and to evaluate the two methods with the relative mean absolute error (RMAE) and bias as evaluation indicators. The results show that compared with the real-time-adjusted Z–R relationship (AZR) scheme and the two-step calibration technique of radar QPE (the AID scheme), STLWLR and STLWLRK with optimization parameters could further improve the accuracy of radar–rain gauge QPE. For 10-fold cross-validation of the cases occurring on Hainan Island from 1 May to 31 October 2018 and for 1-hr, 3-hr, 6-hr, 12-hr, and 24-hr time scales, the RMAE of STLWLRK decreased by 6.3%, 8.4%, 8.4%, 8.2%, and 7.9%, respectively, compared with that of the AID scheme. The results also show that increasing time scale could reduce the error of radar-rain gauge QPE for different methods. As STLWLR is based on solid geography, mathematics, and radar meteorology and has been tested by a large number of cases, STLWLR and its optimization parameters should have certain universality. Overall, compared with traditional methods, STLWLRK is a more robust, easier to implement, more accurate, and less computationally expensive method.

Topographic Amplification of Crustal Subsidence by the Rainwater Load of the 2019 Typhoon Hagibis in Japan

AbstractThe super typhoon Hagibis traveled northeastward through eastern Honshu, Japan, causing disastrous heavy rainfalls along its path on October 11 and 12, 2019. We performed a comprehensive space geodetic study of water brought by this typhoon using a dense network of Global Navigation Satellite System (GNSS) receivers in Japan. First, we studied the time evolution of altitude‐corrected precipitable water vapor field and compare the movement of water vapor centroid with the rain distribution from radar rain gauge analyzed precipitation. The total amount of water vapor derived by spatially integrating precipitable water vapor on land remained steady at ∼20 Gt. The total precipitation by this typhoon was ∼92, and ∼33 Gt of it fell onto the land area of eastern Honshu. Next, we studied crustal subsidence caused by the typhoon rainwater as surface load. The GNSS stations located under the typhoon path temporarily subsided 1–2 cm on the landfall day and the subsidence mostly recovered on the next day. Using the vertical crustal movement data, we estimated the distribution of surface water in eastern Honshu assuming the layered spherical earth. The amount of the surface load on October 12 was ∼71 Gt, which significantly exceeds the cumulative rainfall on land. We consider that the excess subsidence largely originates from the selective deployment of GNSS stations in the concave topography, for example, along valleys and within basins, in the mountainous Japanese Islands.

Improving quantitative precipitation estimates by radar-rain gauge merging and an integration algorithm in the Yishu River catchment, China

High-precision areal rainfall is crucial for hydrometeorological coupled forecasts. The accuracy of quantitative precipitation estimates (QPE) is improved by merging radar-rain gauge data with an integration approach based on a statistical weight matrix in the Yishu River catchment, China. First, a local Z-R relationship (Z = 85R1.82) is reconstructed using a genetic optimization algorithm to minimize the error from different precipitation patterns and climate zones. Next, based on the local Z-R relationship, six methods of merging radar-rain gauge data are respectively adapted to improve the accuracy of QPE, as follows: mean field bias (MFB), Kalman filter (KLM), optimum interpolation (OPT), variation method (VAR), two-step calibration of KLM and OPT (KOP), and two-step calibration of KLM and VAR (KVR). The results indicate that QPE accuracy is clearly improved, and is in good agreement with rain gauge observations, after the six merging methods are applied. Among these methods, KOP performs the best, reducing the mean relative error from 55.2 to 15.1%. An innovative aspect of this work is the inclusion of an integrated ideology based on a statistical weight matrix, which further improves the accuracy of QPE by incorporating the advantages of each estimation mode. The results further show that the accuracy of QPE derived from the integration approach is higher than that obtained by any individual method; QPE values are similar to those obtained the automatic rain gauge network in both the spatial distribution and location of the intense precipitation centers, and better reflects the precipitation status over the ground surface. This approach could serve as a promising conventional method for QPE in the study region.

Pattern evaluation of operational radar reflectivity–rainfall relationship for Quitandinha River flooding events: Petrópolis, Rio de Janeiro (Brazil)

Real-time rainfall distribution is required for decision-making based on the support of warning systems, especially in situations of imminent floodings and landslides. In this work, six relationships on radar reflectivity–rainfall relationships were calibrated and evaluated for 33 rainfall events associated with Quitandinha River flooding with occurred between 2013 and 2016. These events were chosen with the purpose of finding new relationships that could characterize with better accuracy the properties of storms and precipitation associated with Quitandinha floods. The procedure of applying variations of 5%, 10%, and 20% in reflectivity band-pass filter was used to minimize the different types of uncertainties associated with the measurement of radar reflectivity and rain gauges. Multiple linear regression analysis approach showed that radar reflectivity–rainfall relationships which were calibrated at 20% presented the lowest dimensionless coefficient of variability (CV), systematic error (BIAS), mean absolute deviation MAD), and root-mean-square error (RMSE). The calibrated expression between Marshall–Palmer and Nexrad showed the best performance among the six relationships examined.

Can Weather Radars Be Used to Estimate Snow Accumulation on Alpine Glaciers? An Evaluation Based on Glaciological Surveys

AbstractThe snow water equivalent (SWE) is a key component for understanding changes in the cryosphere in high mountain regions. Yet, a reliable quantification at a high spatiotemporal resolution remains challenging in such environments. In this study, we investigate the potential of an operational weather radar–rain gauge composite (CombiPrecip) to infer the daily evolution of SWE on seven Swiss glaciers. To this end, we validate cumulative CombiPrecip estimates with glacier-wide manual SWE observations (snow probing, snow pits) obtained around the time of the seasonal peak during four winter seasons (2015–19). CombiPrecip underestimates the end-of-season snow accumulation by factors of 2.2 up to 3.7, depending on the glacier site. These factors are consistent over the four winter seasons. The regional variability can be mainly attributed to the empirical visibility of the Swiss radar network within the Alps. To account for the underestimation, we investigate three approaches to adjust CombiPrecip for the applicability to glacier sites. Thereby, we combine the factor of underestimation with a precipitation-phase parameterization. For further comparison, we apply a rain gauge catch-efficiency function based on wind speed. We validate these approaches with 14 manual point observations of SWE obtained on two glaciers during three winter seasons. All approaches show a similar improvement of CombiPrecip estimates. We conclude that CombiPrecip has great potential to estimate SWE on glaciers at a high temporal resolution, but further investigations are necessary to understand the regional variability of the bias throughout the Swiss Alps.

Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

A Bayesian Framework for the Multiscale Assessment of Storm Severity and Related Uncertainties

AbstractThis article proposes a statistical framework for assessing the multiscale severity of a given storm at a given location. By severity we refer to the rareness of the storm event, as measured by the return period. Rather than focusing on predetermined spatiotemporal scales, we consider a model assessing the return period of a storm event observed across the continuum of durations and areas around a focus location. We develop a Bayesian intensity–duration–area–frequency model based on extreme value distribution and space–time scale invariance hypotheses. The model allows us to derive an analytical expression of the return period for any duration and area, while the Bayesian framework allows us by construction to assess the related uncertainties. We apply this framework to high-resolution radar–rain gauge reanalysis data covering a mountainous region of southern France during the autumns 2008–15 and comprising 50 rain events. We estimate the model at two grid points located a few kilometers apart on either side of the mountain crest, considering spatiotemporal scales ranging over 3–48 h and 1–2025 km2. We show that at all scales and for all significant events, the return period uncertainties are skewed to the right, evidencing the need of considering uncertainty to avoid systematic risk underestimation. We also reveal the large variability of the storm severity both at short distance and across scales, due to both the natural variability of rainfall and the mask effect induced by the mountain crest.

The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites

This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from ten different earth-observing satellites. The primary validation was conducted for the polar-orbiting satellites SNPP, NOAA20 (both bearing the ATMS instrument), MetopB, and MetopC (both bearing the AMSUA and MHS instruments) during the period from December 1, 2018 through December 31, 2019. Validation was conducted using operational ground-based radar-rain gauge analyses from Stage-IV and multiradar multisensor (MRMS) over the continental United States. Results indicate that the precipitation estimates are largely consistent with one another and that agreement with ground-based analyses has a strong seasonal component. Warm season performance is generally higher and more stable than during the cold season. SNPP and NOAA20 estimates appeared to show slightly higher biases, outside of July and August. Frequency distributions of precipitation intensity also showed better agreement between MiRS and Stage-IV for higher precipitation rates in the warm season, highlighting the difficulty of estimating over land precipitation rates associated with stratiform precipitation systems that typically occur during the cold season. All satellites depicted the annual mean precipitation rate global distribution with good interconsistency, but with some differences possibly related to individual temporal sampling characteristics of each satellite. Summary validation statistics and scores stratified by season show that MiRS performance metrics using MRMS as a reference are largely similar to those based on using Stage-IV.

A method for real‐time temporal disaggregation of blended radar–rain gauge precipitation fields

AbstractA fully automated quasi‐real‐time method is presented to disaggregate hourly to sub‐hourly precipitation information operationally in a blended radar–rain gauge product. The method proposes a fully automated solution to disaggregate precipitation in regions characterized by measurement errors or partial absence of auxiliary information on the temporal precipitation evolution. The solution relies on a combination of low‐pass filtered radar information and stochastically generated noise fields. A comprehensive validation of the new method is provided demonstrating higher skill compared to a uniform disaggregation in time. The method is now an integral part of CombiPrecip, the official operational code of MeteoSwiss for radar–rain gauge merging.

Tropical Andes Radar Precipitation Estimates Need High Temporal and Moderate Spatial Resolution

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

Improving Spatial Rainfall Estimates at Mt. Merapi Area Using Radar-Rain Gauge Conditional Merging

Rainfall monitoring is important for providing early warning of lahar flow around Mt. Merapi. The X-band multi-parameter radar developed to support these warning systems provides rainfall information with high spatial and temporal resolution. However, this method underestimates the rainfall compared with rain gauge measurements. Herein, we performed conditional radar-rain gauge merging to obtain the optimal rainfall value distribution. By using the cokriging interpolation method, kriged gauge rainfall, and kriged radar rainfall data were obtained, which were then combined with radar rainfall data to yield the adjusted spatial rainfall. Radar-rain gauge conditional merging with cokriging interpolation provided reasonably well-adjusted spatial rainfall pattern.

Precipitation Redistribution Method for Regional Simulations of Radioactive Material Transport During the Fukushima Daiichi Nuclear Power Plant Accident

AbstractTo reproduce more accurate deposition maps of radioactive materials released in the Fukushima Daiichi Nuclear Power Plant accident in March 2011, our study focuses on the uncertainty of atmospheric transport simulations caused by precipitation. In our new method, simulated wet deposition distribution of 137Cs is modified by high‐resolution radar rain gauge data of observed precipitation. Sensitivity experiments are conducted to examine the impact of using both different reanalyzed meteorological data sets as boundary condition and the observation data of precipitation as the redistribution of simulated precipitation. Among the results, the experiment modified by high‐resolution radar rain gauge data realized the most accurate cumulative 137Cs deposition from 18 to 27 March. While the meteorological field is reasonably simulated in the atmospheric transport model in the experiments, the results showed that applying observed precipitation also contributes to improve the accuracy of simulated wet deposition amount.

Rain Behaviour at Mt. Merapi Area as Observed by XMPR and ARR

Short duration rainfall information has now become one of many important aspects to support the development of warning criteria for disaster mitigation. Similar importance is also found in the development of warning criteria against the lahar flow disaster at Mt. Merapi area. The rainfall information obtained from the radar observation has also become a new challenge for the last decade in line with the rapid growth of information and communication technology. However, the accuracy of its estimation needs to be evaluated by considering the correlation between radar rainfall and rain gauge rainfall. In case of radar rainfall can be precisely estimated, this information will contribute to generating appropriate warning criteria. This study was carried out as the first attempt to evaluate the rainfall information as performed by the X-Band Multi Parameter Radar (XMPR) that was installed at Mt. Merapi in the mid-August 2015. Several ground rainfall data obtained from Automatic Rainfall Recorder (ARR) have been adopted to analyze the aforesaid radar rainfall information, and estimated errors between the two are presented. Evaluation of the radar estimated error value as a function or range is taken through a Fractional Standard Error (FSE) index that quantifies the differences between ground rainfall measurement (G) and radar rainfall estimation (R), also the G/R ratio characteristics. The result shows there was a poor correlation between radar estimated and rain gauge measured rainfall located over 14 km from radar. Radar bias (M) is suitable for correcting radar rainfall amount, yet inappropriate for fractional values.

Analysis of the 1 November 2015 heavy rainfall episode in Algarve by using weather radar and rain gauge data

The trigger for the study presented in this paper was the extreme rain event of 1 November 2015 in Algarve region. The main objective was the analysis and improvement of the precipitation field using a radar–rain gauge merging method. Ordinary kriging with radar-based error correction has been applied to hourly values of precipitation from both sensors. The merging technique allowed keeping the better radar spatial pattern, being the respective estimates corrected by the rain gauges observations. The procedure led to a reduction in the errors of the precipitation estimates, evaluated by cross-validation, when compared to univariate interpolation of rain gauge observation or radar rain product. Finally, some discussion is also added on the problematic of flooding in urban areas, especially those with absent or deficient urban planning.