Rain Gauge vs. Radar Measurements - Modelling an Extreme Rain Event with High Spatial Variability

The near-real-time legacy product of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (3B42RT) and the equivalent products of Integrated Multi-satellite Retrievals for Global Precipitation Measurement mission (IMERG-E and IMERG-L) were evaluated and compared over Mainland China from 1 January 2015 to 31 December 2016 at the daily timescale, against rain gauge measurements. Results show that: (1) Both 3B42RT and IMERG products overestimate light rain (0.1–9.9 mm/day), while underestimate moderate rain (10.0–24.9 mm/day) to heavy rainstorm (≥250.0 mm/day), with an increase in mean (absolute) error and a decrease in relative mean absolute error (RMAE). The IMERG products perform better in estimating light rain to heavy rain (25.0–49.9 mm/day), and heavy rainstorm, while 3B42RT has smaller error magnitude in estimating light rainstorm (50.0–99.9 mm/day) and moderate rainstorm (100.0–249.9 mm/day). (2) Higher rainfall intensity associates with better detection. Threshold values are <2.0 mm/day, below which 3B42RT is unreliable at detecting rain; and <1.0 mm/day, below which both 3B42RT and IMERG products are more likely to cause false alarms. (3) Generally, both 3B42RT and IMERG products perform better in wet areas with relatively heavy rainfall intensity and/or during wet season than in dry areas with relatively light rainfall intensity and/or during dry season. Compared with 3B42RT, IMERG-E and IMERG-L constantly improve performance in space and time, but it is not obvious in dry areas and/or during dry season. The agreement between IMERG products and rain gauge measurements is low and even negative for different rainfall intensities, and the RMAE is still at a high level (>50%), indicating the IMERG products remain to be improved. This study will shed light on research and application during the transition in multi-satellite rainfall products from TMPA to IMERG and future algorithms improvement.

Variability of the spatial structure of intense Mediterranean precipitation

Abstract. Extreme environmental events, such as floods, droughts, rainstorms, and strong winds have severe consequences for human society. Changes in extreme weather and climate events have significant impacts and are among the most serious challenges to society in coping with a changing climate. The cost of damage caused by extreme climate events is rising all over the world. The European Environment Agency (EEA) report ("Climate Change, Impacts and Vulnerabilities in Europe 2012") stated that the cost of damage had increased from € 9 billion in the 1980s to € 13 billions in the 2000s. In the United States, the National Oceanic and Atmospheric Administration (NOAA) reported that $ 188 billion in damage was caused by the severe weather events in 2011 and 2012. Understanding and identifying hydrometeorologic extreme events and their changes through time are key in sustaining agriculture and socio-economic development. Planning for weather-related emergencies, agricultural and reservoir management and insurance risk calculations, all rely on knowledge of the frequency of these extreme events. The assessment of extreme precipitation is an important problem in hydrologic risk analysis and design. Erosion and removal of the fertile soil layer through hydroclimatic extreme events is also a serious problem in semi-arid to arid regions, especially in mediterranean climates. Accurate measurements of precipitation on a variety of space and time scales are important to climate scientists and decision makers, including hydrologists, agriculturalists and emergency managers. The historical record of precipitation observations is limited mostly to land areas where rain gauges can be deployed, and measurements from those instruments are sparse over large and meteorologically important regions of the Turkey, such as over the Southeastern Anatolia Region. While rain gauge measurements are often used to tune hydrologic models, they are limited by their spatial coverage. Remote sensing techniques using spaceborne sensors provide an excellent complement to continuous monitoring of rain events both spatially and temporally. In this study we compare ground-station data with Tropical Rainfall Measurement Mission (TRMM) products at the 3-hour time scale to evaluate satellite rain estimates for agricultural and hydrological applications in Turkey. The remote sensing dataset TRMM product 3B42 has been validated with daily rain gauge measurements in order to characterize rainfall variability in the Southeastern Anatolia region. The precipitation retrievals from the TRMM satellite were compared with data from 7 surface rain gauges within the period of 1998–2012. Spatiotemporal patterns through statistical analyses were identified by fitting Generalized Extreme Value (GEV) rainfall distribution to the rainfall time series, and the fitting results were analyzed focusing on the behaviour of the shape parameter. Spatial patterns and correlations of rainfall events across the study area were also analysed by the calculation of the 90th, 95th and 99th percentiles. Regional frequency relationship were developed using the chosen GEV distribution. The recurrence intervals for different years have been estimated using the GEV distribution and their spatial variability has been described. The recurrence intervals of large rainstorms have also been identified for the rain gauge stations with the related TRMM pixel time series and spatial patterns in the study area have been evaluated. Preliminary results indicate that there exist large discrepancies between rain gauge and TRMM data at mean rainfall values; however, least squares fits indicate reliable and quite linear correlation for the 90th, 95th, 99th percentiles (r2 = 0.70, 0.77, and 0.75 respectively) and the annual maximum daily amount of precipitation (r2 = 0.69). In other words, TRMM product 3B42 can be used to assess first-order rainfall statistics and recurrence intervals, but rainfall magnitudes vary significantly from ground measurements.

Application of X- and S-band radars for rain rate estimation over an urban area

Neural network (NN) is a nonparametric method to represent the relation between radar measurements and rainfall rate. The relation is derived directly from a dataset consisting of radar measurements and rain gauge measurements. Tropical Rainfall measuring Mission (TRMM) Precipitation Radar (PR) is known to be the first observation platform for mapping precipitation over the tropics. TRMM measured rainfall makes a significant contribution to the study of precipitation distribution over the globe in the tropics. Ground validation (GV) is a critical component in the TRMM system. However, the ground sensing systems have quite different characteristics from TRMM in terms of resolution, scale, sampling, viewing aspect, and uncertainties in the sensing environments. In this paper a novel hybrid NN model is presented to train ground radars for rainfall estimation using rain gauge data and subsequently the trained ground radar rainfall estimation to train TRMM/PR observation based neural networks. This hybrid NN model provides a mechanism to link between gauges on the ground, the ground radar observations and the TRMM/PR observations. The dual-polarization radar measurements from a ground WSR-88DP site in Dallas-Fort Worth region and local rain gauge data will be used for the demonstration purpose. The performance of the rainfall product derived for TRMM PR is then compared against TRMM standard rainfall products. In addition, a direct gauge comparison study is done to examine the improvement brought in by this hybrid neural networks approach.

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

This paper presents a comparison between rain gauge network and weather radar data in Angra dos Reis city, located in the State of Rio de Janeiro (RJ), Brazil. The city has a high incidence of natural disasters, especially associated with heavy rains in densely populated areas. In this work, weather radar data with a spatial resolution of 1 km were obtained from dual-polarimetric S-band radar operated by the Environmental State Institute of Rio de Janeiro (INEA), located in the Guaratiba neighborhood in Rio de Janeiro city, Brazil; the rain gauge measurements were provided by the National Center for Monitoring and Warning of Natural Disasters (CEMADEN), which is composed of a network with 30 rain gauges covering the studied area. The comparison between the two datasets enables the analysis of which radar products better fit the rain gauge network’s accumulated rainfall by quantifying the uncertainties in precipitation estimates at radar pixels where rain gauges are located. The results indicated that radar products generated with the help of regression techniques obtained from the relation between radar reflectivities and rain gauge measurements were a better fit, constituting essential information while dealing with efficient regulation for rainfall monitoring and forecasting to minimize the risks associated with extreme events.

At RF/microwave frequencies below 10 GHz, the atmosphere is considered transparent except for cases of extreme rain events. Moreover, scattering effects from rain drops are generally negligible, and the calculations of extinction coefficients are primarily liquid water absorption. In convective rain events (thunderstorms), larger rain drop sizes would affect both, absorption and scattering coefficients found in rain extinction propagation models. Since 2010, the Central Florida Remote Sensing Laboratory has been conducting field measurements to empirically derive extinction coefficients in strong convective thunderstorms with a zenith pointing radiometer operating at 6.8 GHz. Experimentally, blackbody noise emission from rain (brightness temperature, T b ) is measured simultaneously with rain rate (mm/hr) using surface gauges and the surface rain drop size distribution from a disdrometer. This paper is a theoretical treatment of the signal processing procedures used to derive rain extinction coefficients. Rain gauges, disdrometer and microwave radiometer possess different sampling time events and to align the spatial/temporal domain with the dynamics of a thunderstorm will prove to be challenging. This paper presents several candidate signal analysis approaches through a radiative transfer model (RTM) by incorporating both, rain drop scattering and absorption. The rain gauge and disdrometer measurements are used as inputs into the RTM, to calculate an instantaneous downwelling radiance time series. The radiometer's measured Tbs and RTM theoretical Tbs time series are compared as the rain extinction coefficients are iteratively estimated in a Maximum Likelihood Estimation procedure with statistics to yield a solution. A simulated rain event is illustrated and analyzed.

Abstract. The present study explores the application of a data assimilation (DA) procedure to correct the radar rainfall inputs of an event-based, distributed, parsimonious hydrological model. An extended Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. This work focuses primarily on the uncertainty in the rainfall data and considers this as the principal source of error in the simulated discharges, neglecting simplifications in the hydrological model structure and poor knowledge of catchment physics. The study site is the 114 km2 Lez catchment near Montpellier, France. This catchment is subject to heavy orographic rainfall and characterised by a karstic geology, leading to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because the radar rainfall input to the model depends on geographical features and cloud structures, it is particularly uncertain and results in significant errors in the simulated discharges. This study seeks to demonstrate that a simple DA algorithm is capable of rendering radar rainfall suitable for hydrological forecasting. To test this hypothesis, the DA analysis was applied to estimate a constant hyetograph correction to each of 19 flood events. The analysis was carried in two different modes: by assimilating observations at all available time steps, referred to here as reanalysis mode, and by using only observations up to 3 h before the flood peak to mimic an operational environment, referred to as pseudo-forecast mode. In reanalysis mode, the resulting correction of the radar rainfall data was then compared to the mean field bias (MFB), a corrective coefficient determined using rain gauge measurements. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash-Sutcliffe efficiency criteria compared to the MFB correction. In pseudo-forecast mode, the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge, but uncertainty from the model parameterisation diminishes data assimilation efficiency. While the DA algorithm used is this study is effective in correcting uncertain radar rainfall, model uncertainty remains an important challenge for flood forecasting within the Lez catchment.

Unlike the classical point vortex model, a new method is developed to extract flows induced not only by vorticity but also by divergence in a well-defined vortex core area of a cyclone. This new method is applied to diagnosing the interactions of three midlatitude cyclones (called A, B, and C) that account for a missed summer severe rainfall forecast, in which the daily precipitation predicted by the Canadian operational model is an order of magnitude smaller than the rain gauge and radar measurements. In this event, cyclone B, responsible for the severe rainfall occurrence, was advected largely by flows induced by two neighboring cyclones: A and C to the west and east, respectively. This work attempts to assess whether and to what degree the vertical tilt of the observed cyclone versus that of the forecast cyclone B is caused by advections of the environmental flows (including A- and C-induced flows) at 500 and 1000 hPa. Results show that the observed cyclone B was advected mainly by the cyclone A–induced flow at 500 hPa into a vertically tilted structure that was northwestward against the vertical shear of the environmental flow and thus favorable for upward motion and cyclone intensification around the time of severe rainfall. However, the forecast cyclone B was advected largely by the cyclone A–induced flow at 500 hPa and the cyclone C–induced flow at 1000 hPa into an increasingly northward-tilted structure that was along the vertical shear of the environmental flow and thus unfavorable for upward motion and cyclone intensification, leading to the missed forecast of severe rainfall. Suggestions are made for future improvements of model forecasts.

As heavy rainfall events pose increasing challenges to society, ranging from localised flash floods to widespread flooding that disrupts critical infrastructure and services, accurate and comprehensive analyses of their patterns play a pivotal role in enhancing our understanding of rainfall dynamics, assessing associated risks, and implementing effective mitigation strategies. Among the most practical outcomes of these analyses are depth-duration-frequency (DDF) curves, which serve as a key reference point for the planning and design of water management systems and facilities, such as dams, dikes, flood retention basins, and urban drainage networks. For Germany, DDF curves are provided by KOSTRA-DWD (German: Koordinierte Starkniederschlagsregionalisierung und -auswertung des Deutschen Wetterdienstes), a product that has been developed since the 1980s by the Department of Hydrometeorology at the Deutscher Wetterdienst (DWD).In recent years, KOSTRA-DWD has undergone substantial revisions, incorporating additional rainfall data along with enhanced methods from extreme value statistics and geostatistics. The latest release, KOSTRA-DWD-2020[1], provides DDF curves for 22 durations ranging from 5 minutes to 7 days and 9 return periods from 1 to 100 years, with a spatial resolution of 5 km. While KOSTRA-DWD-2020 relies on annual maximum series derived from 1,900 rain-gauge stations, this study focuses on the development of new multi-sensor heavy rainfall statistics that integrate rain-gauge measurements with high-resolution radar estimates.To this end, we have evaluated a set of radar-supported interpolation schemes aimed at regionalising parameters of the Generalized Extreme Value distribution (μ, σ, ξ), as well as scaling parameters (θ, η) introduced by Koutsoyiannis et al. [2]. All interpolation schemes are based on the method of kriging with external drift, using rain-gauge measurements from 233 stations with long observational records (≥ 50 years) as the primary variable guiding spatial interpolation. Rain-gauge measurements from 1,667 stations with shorter records (≥ 10 years), along with radar estimates, in turn serve as external drift variables that refine the spatial structure of the interpolation. Two radar estimates produced by DWD with high temporal (5 min) and spatial (1 km) resolution have been examined: the first product, RY, consists of quality-controlled radar estimates corrected for beam shielding and processed with refined reflectivity-rainfall relationships, while the second product, YW, is derived from RY through additional quasi-calibration against rain-gauge measurements to reduce systematic bias. Our preliminary results indicate that integrating radar estimates improves the spatial representation of DDF curves, preserving the higher rainfall levels derived from rain-gauge measurements while enhancing spatial variability through radar estimates. However, discrepancies between the different interpolation schemes remain, underscoring the pressing need for a more detailed analysis to better understand their respective strengths and limitations.[1] https://www.dwd.de/DE/leistungen/kostra_dwd_rasterwerte/kostra_dwd_rasterwerte.html[2] Koutsoyiannis et al., 1998, A mathematical framework for studying rainfall intensity‑duration‑frequency relationships. J. Hydrol. (206), 118-135, https://doi.org/10.1016/S0022-1694(98)00097-3

Quality control of rain gauge measurements using telecommunication microwave links

In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km−1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars.

Polarimetric radar has been successful in characterizing cloud and precipitation. Polarization parameters such as radar reflectivity (Z), differential reflectivity (Z_DR), linear reflectivity difference (Z_DP), and specific differential propagation phase shift (K_DP) provide more information about precipitation and allow better characterization of hydrometeors, accurate rain rate estimation, and retrieval of rain drop size distribution (DSD). In the past, rain rate (R) estimation from S-Pol was based on empirical models such as Z-R, R (Z, Z_DR) and R (K_DP) relations, which were derived from regression analyses of radar and rain gauge measurements or numerical simulations, and are prone to errors. In this paper, we derive various rain estimators using a physically-based raindrop size distribution (DSD) model: constrained Gamma DSD. The constrain condition, the shape-slope relation in Gamma DSD, is derived from video-disdrometer observations and has been shown to represent a physical nature of rain and useful in rain DSD retrievals from remote measurements. The relation is then used to retrieve rain DSDs from two remote measurements and t derive rain estimators from polarimetric radar measurements. The derived rain estimators are then used to retrieve rain parameters from S-pol measurements. The results are also verified and compared with ground measurements.

ABSTRACTTo assess the climatology of the Czech Republic (CR) with a high spatial (1 km) resolution, this study uses radar‐based precipitation data collected over the summer seasons of a 10‐year period (2002–2011). Radar reflectivity data were obtained from two C‐band Doppler weather radars, integrated in time and merged with daily precipitation totals from rain gauge measurements. Using radar measurements, daily adjusted precipitation totals were later divided into 10‐min precipitation totals that were continuously accumulated in a series of time windows ranging from 30 min to 24 h. This climatological analysis confirms that altitude substantially influences not only the spatial distribution of mean precipitation but also the spatial distribution of precipitation maxima as well as the diurnal cycle of precipitation in the CR. While the 24‐h totals are generally maximal in mountains, mean and absolute seasonal maxima of short‐term totals (up to 1 and 6 h, respectively) are detected in altitudes between 300 and 600 m a.s.l. Regarding the diurnal cycle, maximum frequency of precipitation occurs 2 h earlier in the mountains, whereas mean totals remain at the same level until 2100 UTC. The mean time during which precipitation maxima occur generally does not change with altitude. Nevertheless, a detailed regional study demonstrates that short‐term precipitation maxima usually start earlier in the afternoon in and around mountainous regions. Long‐term (mainly 6‐h) precipitation maxima occur later than short ones but are substantially less concentrated in time, especially in the mountains. These differences between mountains and lowlands can be explained by smaller relative proportions and earlier onset of convective precipitation in mountains.