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An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremesMagnitude of observed blackouts exceeds climatologically expected valuesUnexpectedly, longer CMLs experience more blackouts [ABSTRACT FROM AUTHOR]

This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the 3-yr data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≥10 mm h−1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain gauge data. Furthermore, this research proposes methods to conduct 2D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the annual precipitation observed by a network of 30 rain gauges in Iraq over a 65-year period (1941–2005). The simulated precipitation from 18 climate models in the CMIP5 project is investigated over the same area and time window. The Mann–Kendall test is used to assess the strength and the significance of the trends (if any) in both the simulations and the observations. Several exploratory techniques are used to identify the similarity (or disagreement) in the probability distributions that are fitted to both datasets. While the results show that large biases exist in the projected rainfall data compared with the observation, a clear agreement is also observed between the observed and modelled annual precipitation time series with respect to the direction of the trends of annual precipitation over the period. [ABSTRACT FROM AUTHOR]

Time-varying relationships between two atmospheric oscillation modes and precipitation (rainfall and snow) in the western portion of the Carpathian Mountains and the northern part of the Pannonian Plain are investigated in this paper. The Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO) modes were used to explain the intra-annual variability of monthly rainfall totals and new snow over a 36-year period (1981-2017) observed at a dense network of 177 rain gauges and 33 snow rods located throughout the investigated area, respectively. The Continuous Wavelet Transform (CWT) was deployed as a band-pass fllter to quantify the spectral coherence and attribution (based on signal-to-noise ratio) of the NAO and AO modes to the precipitation variability. The temporal and spatial patterns of NAO and AO-related signal variance in monthly rainfall totals and fresh snowpack were evaluated on seasonal and monthly bases. The NAO mode explained the major historically observed snow calamities, such as the snowy cold spell of January 1985 that hit central and southern Europe, bringing heavy snowfall to the investigated region and many other parts of Europe. This event was the beginning of a prolonged cold wave in Europe, with extremely low air temperatures across the continent. Our analyses showed that the AO mode appears to have only a limited effect on the overall variability of new snowpack in late winter. The attribution of NAO and AO modes to the variability of monthly rainfall totals is less pronounced compared to new snowpack. [ABSTRACT FROM AUTHOR]

Precipitation displays a remarkable variability in space and time. An important yet poorly documented aspect of this variability is intermittency. In this paper, a new way of quantifying intermittency based on the burstiness B and memory M of interamount times is proposed. The method is applied to a unique dataset of 325 high-resolution rain gauges in the United States and Europe. Results show that the M- B diagram provides useful insight into local precipitation patterns and can be used to study intermittency over a wide range of temporal scales. It is found that precipitation tends to be more intermittent in warm and dry climates with the largest observed values in the southwest of the United States (i.e., California, Nevada, Arizona, and Texas). Low-to-moderate values are reported for the northeastern United States, the United Kingdom, the Netherlands, and Germany. In the second half of the paper, the new metrics are applied to daily rainfall data for 1954-2013 to investigate regional trends in intermittency due to climate variability and global warming. No evidence is found of a global shift in intermittency but a weak trend toward burstier precipitation patterns and longer dry spells in the south of Europe (i.e., Portugal, Spain, and Italy) and an opposite trend toward steadier and more correlated precipitation patterns in Norway, Sweden, and Finland is observed. [ABSTRACT FROM AUTHOR]

This study employs copula functions to establish the dependency structure of the joint distribution among rainfall intensity, wind speed, and wind direction in Qatar. Based on a Vine Copula, the trivariate distribution between rainfall intensity, wind speed, and wind direction is found to exhibit a root-mean-square error (RMSE) of 0.0072 on the observed vs. modeled cumulative probabilities using ranked normalized observations. It is also found that the winter Shamal winds are most pronounced during rainfall. However, a secondary component of easterly winds known as the Kaus winds is also found to exert an important influence. This wind pattern is observable during rainfall at all the selected stations, albeit with minor variations. It is also found that rainfall stations where the rainfall is obstructed in any way from northwest to north and from east to southeast significantly influence the rainfall measurements. Specific rain gauges in Qatar are found to be situated in disrupted surroundings, such as meteorological stations close to passing traffic, where road spray could infiltrate the rain gauge funnel, impacting the accuracy of rainfall measurements. The study results necessitated the relocation of approximately half of these roadside gauges to mitigate wind-induced biases from road spray. An evaluation of operations is recommended for approximately 80 meteorological stations responsible for measuring rainfall in Qatar. The methodology devised in this study holds potential for application to other Middle Eastern countries and regions with similar climates. [ABSTRACT FROM AUTHOR]

In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R( A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011-13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R( A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R( A) rain estimates are compared to R( Z) and R( KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R( Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R( KDP) is used and weaker impact on R( A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R( KDP) or R( A) estimates with R( Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R( A) complemented by R( Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R( KDP) substituted with R( Z) at rain rates below 8 mm h−1. [ABSTRACT FROM AUTHOR]

The response to flood disasters is of great importance to protect people's lives. Proper recognition of the factors affecting floods will lead to the prevention of negative consequences. In this study, three types of Archimedean copulas, including Clayton, Gumbel, and Frank, have been applied to the depth and duration variables of maximum annual precipitation in four rain gauges' data sets (eight variables) throughout the primarily urban eastern catchment of Tehran, Iran. The results indicated that the Gumbel copula is the most suitable function of the Archimedean copulas. The average depth of rainfalls produced by the selected copula increased up to 24% compared with different varieties of single-station scenarios. Also, the average duration of produced rainfalls differed up to 14% difference compared with the single-station scenarios. Finally, the average volume of surface flooding varied between +54% and −154% with respect to the single-station scenarios. As a result, taking into account spatial distribution in rainfall will have a significant impact on the generation of runoff. The overall distribution pattern of runoff is significantly influenced by several factors. Firstly, the simultaneous impact of four rain gauge stations on synthetic runoffs plays a crucial role. The second factor is the spatial distribution of precipitation within the catchment, which is influenced by the distribution of rain gauge stations and the interpolation technique used. Additionally, the routing of synthetic surface runoff through the channel system also contributes to the overall distribution pattern of runoff. [ABSTRACT FROM AUTHOR]

The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the 'forecast phase,' the forecasts from all of the member models use the downscaling coefficients of the 'training phase.' The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble. These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts. [ABSTRACT FROM AUTHOR]

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand. [ABSTRACT FROM AUTHOR]

The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere. Significance Statement: Studies of Earth's climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides. [ABSTRACT FROM AUTHOR]

This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98-2006/07). The evaluated precipitation products are the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals. [ABSTRACT FROM AUTHOR]

The measurement of rainfall has a long history, but despite its apparent simplicity it is difficult to quantify accurately. The common installation of raingauges with rims above the ground surface results in a difference between the rainfall caught and the amount reaching ground level, termed undercatch. The UK standard installation of raingauges is for their rim to be sited at 0.305 m above the ground; however, the use of weighing gauges installed at a minimum rim height of 1 m has increased in recent years. The installation of these weighing raingauges raises complex questions of homogeneity in rainfall data across space and time. Here, we investigate the impact of these changes using field trials of commonly deployed UK raingauges at a site in south-east England. This paper discusses the results of the trial, exploring the variation in and potential drivers of undercatch with differing gauge sitings. With varying standards for gauge heights around the world and new rainfall measurement technologies coming to the market all the time, improved understanding of undercatch is needed to inform evolving operational practices and explore the possibility of developing catch correction algorithms to remove arising inhomogeneity in precipitation datasets. [ABSTRACT FROM AUTHOR]

Convective precipitation is the main cause of extreme rainfall events in small areas. Its primary characteristics are both large spatial and temporal variability. For this reason, the monitoring of accumulated precipitation fields (liquid and solid components) at the surface is difficult to carry out through the use of rain gauge networks or remote sensing observations. Alternatively, numerical models may be a useful tool to simulate convective precipitation for various analyses and predictions. This paper focuses on improving quantitative convective precipitation estimates that are obtained with a cloud-resolving model. This aim is attained by using the appropriate cloud drop size distribution and modified single sounding data. The authors perform comparisons between observations and three model samples of the areal-accumulated convective precipitation for a 15-yr period over mountainous and flat land areas with 45 and 29 convective events, respectively. They compare the results from a numerical cloud model that uses 2 different microphysical schemes--the unified Khrgian--Mazin size distribution of cloud drops--and an alternative scheme that is a combination of a monodispersed cloud droplet spectrum and the Marshall--Palmer size distribution for raindrops. The authors'' statistical analysis shows that the model version with the Khrgian--Mazin size distribution and the new initial conditions better simulates the observed areal-accumulated convective precipitation than the alternative microphysical approach for both study areas. The model simulations with the Khrgian--Mazin size distribution most closely match observations for the flat land area with a correlation coefficient of 0.94, while it is somewhat lower (0.89) for the mountainous area. Use of the alternative microphysical approach, on the other hand, underestimates the observed precipitation, and has the lowest correlation coefficient among the methods, 0.82 for the mountainous area and 0.85 for the flat land. [ABSTRACT FROM AUTHOR]

Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings. [ABSTRACT FROM AUTHOR]

Abstract: The WMO Field Intercomparison of Rainfall Intensity (RI) Gauges started on October, 1st 2007 at Vigna di Valle (Italy) and was concluded in May 2009. Those catching type instruments, out of the selected rain gauges based on various measuring principles, and the four rain gauges selected as reference instruments to be installed in a pit, were preliminarily calibrated in the laboratory before their final installation at the Field Intercomparison site. The recognized WMO laboratory at the University of Genoa was involved in this task, using the same standard tests adopted for the previously held WMO Laboratory Intercomparison of RI gauges. Further tests were performed to investigate the one-minute performance of the involved instruments. The present paper deals with basically Tipping-Bucket Rain gauges (TBRs) and Weighing Gauges (WGs), using results from tests performed under constant flow rates in laboratory conditions. The objective of this initial phase of the Intercomparison was to single out the counting errors associated with each instrument, so as to help the understanding of the measured differences between instruments in the field during the second phase. Results and comments on the preliminary laboratory calibration exercise are reported in this paper together with their implications for the analysis of the outcome of the Intercomparison in the Field. [Copyright &y& Elsevier]

The South Florida Water Management District (SFWMD) relies on a network of nearly 300 rain gauges in order to provide rainfall data for use in operations, modeling, water supply planning, and environmental projects. However, the prevalence of convective and tropical disturbances in South Florida during the wet season presents a challenge in that the current rain gauge network may not fully capture rain events that demonstrate high spatial variability. Next Generation Radar (NEXRAD) technology offers the advantage of providing a spatial account of rainfall, although the quality of radar-rainfall measurements remains largely unknown. The comparison of rainfall estimates from a gauge-adjusted, NEXRAD-based product developed by the OneRain Company with precipitation measurements from SFWMD rain gauges was performed for the Upper and Lower Kissimmee River Basins over a four-year period from 2002 to 2005. Overall, NEXRAD was found to underestimate rainfall with respect to the rain gauges for the study period, demonstrating a radar to gauge ratio of 0.95. Further investigation of bias revealed the tendency for NEXRAD to overestimate small rainfall amounts and underestimate large rainfall amounts relative to the gauge network. The nature of bias present in the data led to the development of a radar-rain gauge relationship to predict radar precipitation estimates as a function of rain gauge measurements. The intent of this paper is to demonstrate the importance of identifying systematic offsets which may be present in radar-rainfall data before application in hydrologic analysis. [ABSTRACT FROM AUTHOR]

Local, short‐duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio‐temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub‐kilometre (750 m) HARMONIE‐Climate model simulation driven by ERA5 reanalysis data. Three convection‐permitting models at 750 m, 3 and 5 km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event‐focused. Precipitation events are analysed at both hourly and sub‐hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub‐kilometre scale simulation seems limited compared to the convection‐permitting models at 3 and 5 km. [ABSTRACT FROM AUTHOR]

Eight years of daily rainfall data from WegenerNet were analyzed by comparison with data from Austrian national weather stations. WegenerNet includes 153 ground level weather stations in an area of about 15 km × 20 km in the Feldbach region in southeast Austria. Rainfall has been measured by tipping bucket gauges at 150 stations of the network since the beginning of 2007. Since rain gauge measurements are considered close to true rainfall, there are increasing needs for WegenerNet data for the validation of rainfall data products such as remote sensing based estimates or model outputs. Serving these needs, this paper aims at providing a clearer interpretation on WegenerNet rainfall data for users in hydro-meteorological communities. Five clusters – a cluster consists of one national weather station and its four closest WegenerNet stations – allowed us close comparison of datasets between the stations. Linear regression analysis and error estimation with statistical indices were conducted to quantitatively evaluate the WegenerNet daily rainfall data. It was found that rainfall data between the stations show good linear relationships with an average correlation coefficient ( r ) of 0.97 , while WegenerNet sensors tend to underestimate rainfall according to the regression slope (0.87). For the five clusters investigated, the bias and relative bias were - 0.97 mm d −1 and - 11.5 % on average (except data from new sensors). The average of bias and relative bias, however, could be reduced by about 80 % through a simple linear regression-slope correction, with the assumption that the underestimation in WegenerNet data was caused by systematic errors. The results from the study have been employed to improve WegenerNet data for user applications so that a new version of the data (v5) is now available at the WegenerNet data portal ( www.wegenernet.org ). [ABSTRACT FROM AUTHOR]

The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase KDP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and KDP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings. [ABSTRACT FROM AUTHOR]

On 1 September 2021, the remnants of Hurricane Ida transformed into a lethal variant of tropical cyclone in which unprecedented short‐duration rainfall from clusters of supercells produced catastrophic flooding in watersheds of the Northeastern US. Short‐duration rainfall extremes from Ida are examined through analyses of polarimetric radar fields and rain gauge observations. Rainfall estimates are constructed from a polarimetric rainfall algorithm that is grounded in specific differential phase shift (KDP) fields. Rainfall accumulations at multiple locations exceed 1000‐year values for 1–3 hr time scales. Radar observations show that supercells are the principal agents of rainfall extremes. Record flood peaks occurred throughout the eastern Pennsylvania—New Jersey region; the peak discharge of the Elizabeth River is one of the most extreme in the eastern US, based on the ratio of the peak discharge to the sample 10‐year flood at the gaging station. As with other tropical cyclones that have produced record flooding in the Northeastern US, Extratropical Transition was a key element of extreme rainfall and flooding from Ida. Tropical and extratropical elements of the storm system contributed to extremes of atmospheric water balance variables and Convective Available Potential Energy, providing the environment for extreme short‐duration rainfall from supercells. Key Points: Remnants of Hurricane Ida produced unprecedented short‐duration rainfall extremes at 1–3 hr time scaleSupercells were the principal agents of extreme rainfall and floodingExtratropical Transition of Ida contributed to the environment of record short‐duration rainfall [ABSTRACT FROM AUTHOR]

The algorithm of the Convective Rainfall Rate with Microphysical Properties (CRRPh) product of the 2021 version of the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) presents innovative characteristics. It was developed employing principal components analysis to reduce the number of utilized parameters and uses the same mathematical scheme for day and night, simulating the missing visual channels and satellite-derived cloud water path information that is unavailable during nighttime. Applying adequate statistical methodologies and scores and using rain gauge data as ground truth, it is shown that the new algorithm appears to be significantly improved compared to its predecessors in regard to the delineation of the precipitation areas. In addition, it minimizes the day–night difference in the estimation efficiency, which is a remarkable achievement. The new product suffers from slightly higher errors in the precipitation accumulations. Finally, it is shown that topography does not seem to affect the estimation efficiency of the product. In light of these results, it is argued that, overall, the new algorithm outperforms its predecessors and, possibly after adequate adaptations, can be used as a real-time total precipitation product. [ABSTRACT FROM AUTHOR]

Water is a basic element of the natural environment and the most important component in human water management. Rainfall is the main source of water. Therefore, determining the amount of precipitation reaching the ground using sensors is crucial information. Precise precipitation data are necessary for better modeling quality, as the observation data from weather stations are used as basics for weather model assessment. The authors compared precipitation from the Hellmann rain gauge (climatic precipitation, 1.0 m above the ground surface) measured throughout the year and the GGI 3000 rain gauge (actual precipitation on the ground level) measured from April to October. Measurement sequences from the years 2011–2020 were considered. The data for analysis were obtained from a weather station located in northern Poland. The authors analyzed the relationships between data from the two sensors. A comparative study showed that the measurements of actual precipitation are higher and there are strong relationships between actual and climatic rainfall (r = 0.99). Using the introduced coefficient it is possible to determine the full–year actual precipitation with high probability, taking into account the precipitation with a correction from the winter half-year and the actual precipitation from the summer half-year, which is of great importance in the calculation of the water balance. [ABSTRACT FROM AUTHOR]

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection. [ABSTRACT FROM AUTHOR]

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, Virginia, has been established as a semipermanent supersite for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research-quality precipitation instruments, including NASA's S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual-Frequency Precipitation Radar (DPR), combined DPR–GPM Microwave Imager (GMI) and GMI level II precipitation retrievals compared to WPRF ground observations from a 6-yr collection of satellite overpasses. Multisensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (Dm), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 min after overpass time but are not consistent between cases. In addition, GPM Core Observatory Dm retrievals are within level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (<1.0 mm h−1). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multisensor DPR/GMI combined algorithms outperform single-sensor DPR algorithm. Significance Statement: Satellites are beneficial for global precipitation surveillance because extensive ground instruments are lacking, especially over oceans. Ground validation studies are required to calibrate and improve precipitation algorithms from satellite sensors. The primary goal of this study is to quantify the differences between satellite raindrop size and rain-rate retrieval with ground-based observations. Rainfall-rate algorithms require assumptions about the mean raindrop size. Results indicate Global Precipitation Measurement (GPM)/satellite-based mean raindrop size is within acceptable error (±0.5 mm) with respect to ground measurements. In addition, GPM satellite measurements overestimate light rain (<1.0 mm h−1), which is important during the winter months and at high latitudes. Illuminating the challenges of GPM satellite-based precipitation estimation can guide algorithm developers to improve retrievals. [ABSTRACT FROM AUTHOR]

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper examines the impacts of rain gauge densities on runoff simulations. [•] Variation of rain gauge densities impacts the areal mean rainfall considerably. [•] The locations of rain gauges impact the model performances considerably. [•] Great impacts on model performances occurring when rain gauges fall below 0.4 station per 1000km2. [Copyright &y& Elsevier]

Abstract: Using 30years (1979–2009) of data from 33 automatic rain gauges in the Netherlands, a study of the space–time variability of rainfall is performed. This study uses 90-day averaged semi-variograms to find seasonal signals in the fitted spherical semi-variogram parameters of rainrates in the Netherlands, for accumulation intervals between 1 and 24h. These signals can be well-described by simple cosine functions. The dependence of these cosine functions on the accumulation interval is modeled in two different ways: (1) power-law relations between the variogram parameters and the accumulation interval, and (2) power-law relations between the parameters of the cosine functions and the accumulation interval. For the first method the cosine function at the 24-h accumulation interval is also needed. The second of these methods has more parameters, but is shown to model the temporal scaling best. The space–time scaling relations found in this paper are compared to those found by others for similar and contrasting climates. Seasonality is shown to play an important role in determining rainfall spatial variability. [Copyright &y& Elsevier]

Abstract: This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales. [Copyright &y& Elsevier]

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The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Zh–kh relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZh and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase Kdp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars. [ABSTRACT FROM AUTHOR]

The Chinese Loess Plateau (CLP) is still undergoing development. Nearly one-third of the geological disasters take place in this region in China each year. Prior to assessing the spatial characteristics of the rainfall-induced landslides in the CLP, it is fundamental to establish a detailed landslide inventory map. This study utilizes GLDAS (Global Land Data Assimilations Systems) rainfall distribution data in the CLP based on yearly, monthly, and the daily precipitation data from 57 rain gauge stations. Geostatistics analysis and Geographic Information System technique combined with information on geological disasters within the region were used to study the spatial characteristics and trend variations of rainfall and geological disaster distribution in the region. The results indicate that a positive relationship exists between geological disaster and the average annual rainfall, correlation coefficient reaches 0.82, with zonality disasters (account for more than 90%) in the loess gullies, hills, and ridges. Seven zones of loess landside distribution were identified based on their landside densities. The spatial distribution of the geological disasters is characterized by a "linear and zone" shape in the CLP. These results provide a useful reference for the study of the mechanism of water-induced loess landslides. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional "grid-by-grid analysis," the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG's accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for "hybrid" data-driven and physics-driven estimates in order to make optimal usage of satellite observations. [ABSTRACT FROM AUTHOR]

Rainfall extreme value analysis provides information that has been crucial in characterizing risk, designing successful infrastructure systems, and ultimately protecting people and property from the threat of rainfall-induced flooding. However, in the Houston region recent events (such as the unprecedented rainfall wrought by Hurricane Harvey) have highlighted the inability of existing analyses to accurately characterize current climate conditions. Specifically, there has been little research investigating how spatial patterns of extreme precipitation have shifted through time in the Texas Gulf Coast region, which has led to mis-characterization of existing intensity–duration–frequency curves. This study investigates spatiotemporal trends in extreme precipitation in southeast Texas using a statistical approach for peaks-over-threshold modeling that employs a generalized Pareto distribution. Precipitation data from over 600 rain gauges across the region are analyzed in 40-year time windows to evaluate shifts in distribution parameters and extreme rainfall levels through time. Spatial analysis of these trends focuses on highlighting regions with increasing, stationary, and decreasing extreme rainfall through time. Results demonstrate heterogeneity in spatiotemporal trends across the entire study region, but significant increases in extreme rainfall over the Houston urban area. Spatial analysis of these trends focuses on how extreme rainfall has changed within different watersheds. Return level estimates of extreme rainfall values are also compared to the current standards for Harris County. Results from this study identify areas that have experienced significant shifts in extreme rainfall, and can help inform where design standards may be inaccurate or outdated. [ABSTRACT FROM AUTHOR]

The evaluation uncertainty caused by a standard reference itself is harmful to both algorithm developers and data users in substantially understanding the error features and the performance of satellite precipitation products (SPPs). In this study, the Climate Precipitation Center Unified (CPCU) data and the Merged Precipitation Analysis (MPA) data are used as the benchmark to investigate the evaluation uncertainties of satellite precipitation estimates generated by the reference itself. Two SPPs, IMERG-Late and GSMaP-MVK, are employed here. The results show that the approach using two different ground-based precipitation products as the references can effectively reveal the potential evaluation uncertainties. Interestingly, it is found that the evaluation results are prone to resulting in larger uncertainties over semihumid areas. Furthermore, evaluation uncertainty of statistical metrics is closely related to rainfall intensity in that it has a gradually decreasing tendency with increasing rainfall intensities. Additionally, we also found that the dependency of the false alarm ratio (FAR) and root-mean-square error (RMSE) scores on the spatial density of rain gauges is relatively low. Both relative bias (RBIAS) and normalized root-mean-square error (NRMSE) scores for light precipitation (1-5mm day-1) increase with the spatial density of the rain gauges, suggesting that the evaluation of light precipitation can easily cause uncertainties relative to medium-to-high rain rates. Finally, the minimum gauge density required for different scores and different rainfall intensities is discussed. This study is expected to provide criteria to investigate the reliability of evaluation results for the satellite quantitative precipitation estimation community. [ABSTRACT FROM AUTHOR]

The recent intensification of extreme rainfall events has the potential to aggravate the frequency and intensity of floods in urban areas and signals the urgent need for accurate and high spatiotemporal resolution precipitation data for flood monitoring and warning to alleviate the associated damages. In this context, this paper aims to statistically assess the latest Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) multisatellite constellation and radar quantitative precipitation estimation (QPE) products against dense rain gauge network observations under multiple temporal scales (hourly and 3-hourly) and three spatial scales (grid cumulative, regional, and grid) over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. The results indicate that (1) at the grid cumulative scale, QPE significantly outperforms IMERG products. The correlation coefficients of QPE (0.83 ≤ CORR ≤ 0.85) are much higher than those of IMERG products (0.35 ≤ CORR ≤ 0.56), the estimation of QPE is generally significantly underestimated (−0.23 ≤ BIAS ≤ -0.16), and the BIAS of IMERG products is larger than that of QPE (−0.24 ≤ BIAS ≤ 0.23). (2) At the regional scale, the accuracy of IMERG products has been significantly improved, with relatively high correlation coefficients (CORR≥0.8) and decreased root mean squared error (RMSE) values (1.18 mm ≤ RMSE ≤ 0.22 mm), and Taylor diagrams visually show that QPE is closer to the gauge observations than are IMERG products. (3) The evaluation results at the grid scale show that both IMERG products and QPE can accurately reflect the spatial distribution of precipitation. Among these results, the spatial distribution of QPE is the most consistent with the actual rainfall, although an obvious underestimation of rainfall occurs in areas with strong rainfall. (4) In terms of detection indicators, the three IMERG products are superior to QPE at capturing rainfall events and have more advantages over QPE in the estimation of instantaneous precipitation, especially for light rain events. However, QPE has better detection effects for rainfall events under rainstorm and heavy rainstorm events, indicating the hydrologic utility of this product. Among the three IMERG products, the IMERG Early Run (ER) product has the advantages of a short lag time and high accuracy, and it has the potential to be applied in flood forecasting. Future studies should improve IMERG algorithms and promote the application of QPE in urban rainstorm and flood research. • The quality of the IMERG products and radar quantitative precipitation estimation (QPE) is compared over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. • The dense rain gauge network observations are used for evaluation in the GBA. • The evaluation of multisource precipitation products under different temporal and spatial scales has different results and effects. [ABSTRACT FROM AUTHOR]

The amount and distribution of precipitation plays a vital role in the management of water resources, agriculture and flood-risk preparedness. Unfortunately, Zambia like many other developing countries is a highly data-scarce country with few and unevenly distributed meteorological stations. The objective of this study was to run a comparative analysis of satellite-based rainfall products (SRPs) and gauge data to ascertain the reliability of using SRPs for daily rainfall measurements in Zambia. The four daily SRPs examined in this study include the following: The Tropical Applications of Meteorology using Satellite and ground-based observations version 3 (TAMSATv3), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), the Climate Hazards group InfraRed Precipitation with Station data version 2 (CHIRPSv2.0), and the African Rainfall Climatology Version 2 (ARCv2). SRPs were compared to rain gauge data from 35 meteorological, agrometeorological, and climatological stations in Zambia for the period 1998–2015. Statistical analyses were extensively carried out at temporal scales inter alia daily, monthly, seasonal and annual. Comparisons were also done for three stations lying at the highest, middle and lowest elevations to examine the ability of SRPs to capture precipitation occurrences on complex topography. Strong coefficient of determination (> 0.9) of all the SRPs and gauge data were found at the monthly scale even over multifaceted topography. However, the ability of these products to capture rain gauge data at daily, seasonal and annual scales differs markedly. Specifically, PERSIANN outperforms all the other SRPs at all scales, CHIRPSv2.0 is rated second, followed by TAMSATv3 and ARCv2, respectively. These results suggest that PERSIANN can reliably be used in studies that seek to estimate rainfall in data-sparse regions of Zambia at any temporal scale and arrive at similar results to rain gauge data. [ABSTRACT FROM AUTHOR]

The study evaluated a numerical weather model (WRF)-based satellite precipitation adjustment technique with 81 heavy precipitation events that occurred in three tropical mountainous regions (Colombia, Peru, and Taiwan). The technique was applied on two widely used near-real-time global satellite precipitation products—the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Global Satellite Mapping of Precipitation project (GSMaP)—for each precipitation event. The WRF-adjusted satellite products along with the near-real-time and gauge-adjusted satellite products as well as the WRF simulation were evaluated by independent gauge networks at daily scale and event total scale. Results show that the near-real-time precipitation products exhibited severe underestimation relative to the gauge observations over the three tropical mountainous regions. The underestimation tended to be larger for higher rainfall accumulations. The WRF-based satellite adjustment provided considerable improvements to the near-real-time CMORPH and GSMaP products. Moreover, error metrics show that WRF-adjusted satellite products outperformed the gauge-adjusted counterparts for most of the events. The effectiveness of WRF-based satellite adjustment varied with events of different physical processes. Thus, the technique applied on satellite precipitation estimates of these events may exhibit inconsistencies in the bias correction. [ABSTRACT FROM AUTHOR]

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual- Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5-15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for bothDPRKu and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products. [ABSTRACT FROM AUTHOR]

Tipping-bucket rain gauges are used widely to measure the amount and intensity of gross rainfall and throughfall in forests, despite the fact that their systematic underestimations are well known. To the knowledge of the authors, no dynamic calibrations for the budget-conscious Davis gauge (Rain Collector II, Davis Instruments, California) have been published. Thus, five Davis gauges were dynamically calibrated under different constant intensities of inflow and a correction equation was derived. The derived correction equation for the Davis rain gauge is V=-0.2005Q2+0.702Q+1 (R2=0.95, p<0.001), where V is the actual volume of a single tip scaled by the static volume of single tip c (cm3 cm-3), and Q is actual inflow scaled by c (s-1). The Davis rain gauge was then compared to the Onset rain gauge and the Ota rain gauge, and the corrections were applied to field observation data of canopy interception loss from a temperate forest in Japan. It is necessary to apply corrections to gross rainfall and throughfall data by tipping-bucket gauges because the results reveal that such corrections change the actual interception loss computed by values from 220% to 40%, depending on the combination of gauges employed. This difference is not trivial. The systematic bias of the Davis gauge is larger than the Onset and Ota gauges. Thus, it is recommended that researchers using Davis rain gauges apply the dynamically calibrated correction equation presented here to ensure more reliable estimates of gross rainfall and canopy interception loss. [ABSTRACT FROM AUTHOR]

The diurnal variation of rainfall over China associated with landfalling tropical cyclones (TCs) is investigated using hourly rain gauge observations obtained from 2425 conventional meteorological stations in China. Records between 12 h prior to landfall and 12 h after landfall of 450 landfalling TCs in China from 1957 to 2014 are selected as samples. The harmonic analysis shows an obvious diurnal signal in TC rainfall with a rain-rate peak in the early morning and a minimum in the afternoon. The diurnal cycle in the outer region (between 400- and 900-km radii from the storm center) is found to be larger than in the core region (within 400 km of the storm center). This could be attributed to the effect of land on the inner core of the storms as the diurnal cycle is distinct in the core region well before landfall. As the result of this diurnal cycle, TCs making landfall at night tend to have cumulative precipitation, defined as the precipitation cumulated from the time at landfall to 12 h after landfall, about 30% larger than those making landfall around noon or afternoon. Moreover, the radial propagation of the diurnal cycle in TC rain rate, which has been a controversial phenomenon in some previous studies with remote sensing observations, was not present in this study that is based on rain gauge observations. Results also show that the diurnal signal has little dependence on the storm intensity 12 h prior to landfall. [ABSTRACT FROM AUTHOR]

ABSTRACT In the last few years, there has been an increasing application of gridded precipitation data sets for studies analysing seasonal and interannual precipitation variability as well as for performing validation analysis of climate and regional models. Consequently, the purpose of the present study is to analyse the representation of temporal variability and spatial patterns of precipitation in South America for the period 1971-2010, derived by three data sets based exclusively on rain gauge information (Climate Research Unit - CRU, Global Precipitation Climatology Centre - GPCC and University of Delaware - UDel), in order to identify discrepancies and/or agreements among them. Further analysis is performed for three particular sub-regions within the continent (southern sector of southeastern South America - SSESA, central South America - CSA and southeastern Brazil - SEBR). Results show that the three products represent key spatial features of precipitation analogously. Significant coherence in capturing the temporal variability, associated with the dominant modes of variability, is found in all sub-regions, though they differ largely in some areas, especially in CSA. Overall, depending on the region, the gridded products behave differently, without one particular data set standing out as an outlier in all the analysed cases. [ABSTRACT FROM AUTHOR]

Surface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces. [ABSTRACT FROM AUTHOR]

Using 92-year precipitation data from 400 rain gauge stations located in 48 states throughout the continental United States, the change in precipitation in space and time was investigated. The variability in precipitation was investigated using an entropy-based approach. The Mann-Kendall, Spearman's rho, and Sen slope tests were applied to assess the existence of a trend in precipitation. Analysis showed that annual precipitation exhibited lower temporal variability than did its constituent seasonal series. The fall precipitation had the highest variability, and the spring precipitation the lowest. Not many stations exhibited trends, and they are concentrated in limited areas of the United States. The greatest part of those exhibiting statistically significant trends showed increasing trends both in precipitation and rainy days. Only in a few cases negative trends were found. Finally, analysis showed that no relationship can be established between trend and variability in precipitation. [ABSTRACT FROM AUTHOR]

The Next-Generation Weather Radar (NEXRAD) system digital storm-total precipitation product (DSP; at 4-7-min intervals) is examined for its quality in terms of precipitation quantity and spatial rainfall distribution. Observations from a network of 50 rain gauges in the Upper Guadalupe River Basin are compared to the DSP rainfall estimates from two radars: KEWX at New Braunfels, Texas, and KDFX (14 km east of Brackettville, Texas), for the period of September 2006 to June 2008. The rainfall data comparisons are conducted at three temporal scales: 6 min, 1 h, and storm-total accumulations, and at different distances from the radars, from near () to middle (50-100 km), far (100-160 km), and very far ranges (). A strong range dependence is found from radar estimates, i.e., radar underestimates at near and very far ranges ( and ), matches well or slightly overestimates at middle ranges (50-100 km), and overestimates at far ranges (100-160 km). The correlation coefficients between paired radar and gauge precipitation estimates are moderate ( from 0.62 to 0.76) for both radars at the three time scales, however, they are spatially different (at the county level, showing a decreasing trend from middle to far ranges, passing west to east through Kerr, Kendall, Comal, and Guadalupe counties) under the KDFX umbrella. Under the KEWX umbrella, there is no trend of spatial correlation coefficients from county to county. Similarly, there is an overall increase of mean relative difference between radar and gauge values from middle to far ranges from the KDFX radar, but mixed results for the KEWX radar. The probabilities of rainfall detection (POD) for both radars are greater than 90% and are always higher than the gauge PODs, which increase from at the 6-min temporal scale, to at the hourly scale, to for the storm-total scale. There is no range dependence for POD. Overall, the DSP product suffers from severe range dependence, and its rainfall estimates agree better with rain gauge estimates during the warm season than during the cold season. However, the DSP's advantages in high temporal and spatial resolutions and the near real time storm-total rainfall estimations provide great potential for near real time flash flood monitoring and forecasting at local and regional scales, although it still needs more testing and corrections, especially at very close ranges, far ranges, and during the cold season. [ABSTRACT FROM AUTHOR]

The Dutch weather radars in De Bilt and Den Helder have only limited coverage in the Dutch-German border area in the northeastern part of the Netherlands, whereas the German radar in Emden is just across the border. For many years, local water authorities have been looking for a better quantitative precipitation estimate (QPE) for this region. Recently, a German water-management consultancy and a Dutch one have jointly taken up the challenge to develop a completely new precipitation radar composite for this part of the Netherlands. The new composite uses the basic polar radar products (volume data) of the two national weather bureaus. The composite should be able to meet precipitation-information requirements of operational water managers; in other words, it should be able to provide QPE in real time. The present case study of an interesting rainfall event demonstrates the capabilities of the new composite tool. The rainfall event was used to evaluate various filtering and correction algorithms. QPE results were verified against independent rain-gauge data. On average, the results of the new composite were found to be similar to the Dutch weather bureau's QPE for the entire Netherlands. However, the new composite yielded a much better QPE for the northeastern part of the country, as a result of the addition of the Emden radar data. The algorithms developed are ready to be applied in operational water management by water boards and municipalities in the northeastern part of the Netherlands. [ABSTRACT FROM AUTHOR]

The main objective of the study is to examine the accuracy of and differences among simulated streamflows driven by rainfall estimates from a network of 22 rain gauges spread over a 2,170 km2 watershed, NEXRAD Stage III radar data, and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite data. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physically based, distributed parameter, grid-structured, hydrologic model, was used to simulate the June-2002 flooding event in the Upper Guadalupe River watershed in south central Texas. There were significant differences between the rainfall fields estimated by the three types of measurement technologies. These differences resulted in even larger differences in the simulated hydrologic response of the watershed. In general, simulations driven by radar rainfall yielded better results than those driven by satellite or rain-gauge estimates. This study also presents an overview of effects of land cover changes on runoff and stream discharge. The results demonstrate that, for major rainfall events similar to the 2002 event, the effect of urbanization on the watershed in the past two decades would not have made any significant effect on the hydrologic response. The effect of urbanization on the hydrologic response increases as the size of the rainfall event decreases. [ABSTRACT FROM AUTHOR]

The nonhomogeneous spatial activation of raincells (NSAR) model is presented which provides a continuous spatial-temporal stochastic simulation of rainfall exhibiting spatial nonstationarity in both amounts and occurrence. Spatial nonstationarity of simulated rainfall is important for hydrological modeling of mountainous catchments where orographic effects on precipitation are strong. Such simulated rainfall fields support the current trend toward distributed hydrological modeling. The NSAR model extends the Spatial Temporal Neyman-Scott Rectangular Pulses (STNSRP) model, which has a homogeneous occurrence process, by generating raincells with a spatially nonhomogeneous Poisson process. An algorithm to simulate nonhomogeneous raincell occurrence is devised. This utilizes a new efficient and accurate algorithm to simulate raincells from an infinite 2-D Poisson process, in which only raincells relevant to the application are simulated. A 4009 km2 Pyrenean catchment exhibiting extreme orographic effects provides a suitable case study comprising seven daily rain gauge records with hourly properties estimated using regional downscaling relationships. Both the NSAR and the STNSRP models are fitted to five calibration rain gauges. Simulated hourly fields are validated using the remaining two rain gauges providing the first validation of time series sampled from STNSRP or NSAR processes at locations not used in model fitting. The NSAR model exhibits considerable improvement over the STNSRP model particularly with respect to nonhomogeneous rainfall occurrence at both daily and hourly resolutions. Further, the NSAR simulation provides an excellent match to the spatially nonhomogeneous observed daily mean, proportion dry, variance, coefficient of variation, autocorrelation, skewness coefficient, cross correlation and extremes, and to the hourly proportion dry and variance properties. [ABSTRACT FROM AUTHOR]

This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas. [ABSTRACT FROM AUTHOR]

The article discusses a study on the spatial variability in precipitation within the Hilton Experimental Site in Shropshire, England from 1982 to 2006. Results of the study revealed a notable difference between two adjacent rain gauges, with a mean difference of 3.3% of the total. Also, intra-site variability was found to be influenced mainly by spatial variations in wind turbulence. While basal sections had 2.5-7.9% more precipitation, upper gauges received less due to turbulence caused by increased exposure on the top of the slope resulting to precipitation being carried over the gauge orifice.