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Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydrometeorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR), can estimate precipitation and cloud properties and have a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss–Waldvogel disdrometers from 2014 to 2022. The precipitation and drop size distribution parameters like rainfall rate (R; mm h−1), radar reflectivity factor (dBZ), mass-weighted mean drop diameter (Dm; mm), and normalized intercept parameter (Nw; m−3 mm−1) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5-km average, 10-km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR's operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain. [ABSTRACT FROM AUTHOR]

To improve the accuracy of quantitative precipitation estimation (QPE) in complex terrain, a new rainfall rate estimation algorithm has been developed and applied on two C-band dual-polarization radars in Taiwan. In this algorithm, the specific attenuation A is utilized in the rainfall rate R estimation, and the parameters used in the R( A) method were estimated using the local drop size distribution (DSD) and drop shape relation (DSR) observations. In areas of complex terrain where the lowest antenna tilt is completely blocked, observations from higher tilts are used in radar QPE. Correction of the vertical profile of rain rate estimated by the R( A) algorithm (VPRA) is applied to account for the vertical variability of rain. It has been found that the VPRA correction improved the accuracy of estimated rainfall in severely blocked areas. The R( A)-VPRA scheme was tested for different precipitation cases including typhoon, stratiform, and convective rain. Compared to existing rainfall estimation algorithms such as rainfall-reflectivity ( R- Z) and rainfall-specific differential phase ( R- KDP), the new method is able to provide accurate and robust rainfall estimates when the radar reflectivity is miscalibrated or significantly biased by attenuation or when the lower tilt of the radar beam is significantly blocked. [ABSTRACT FROM AUTHOR]

Scale differences may introduce a bias when comparing, merging, or assimilating rainfall measurements because the dynamic range of values representing the underlying physical process strongly depends on the resolution of the data. The present study addresses this issue from the perspective of how well coarser-resolution radar-rainfall observations may be used for evaluation of hydrologic point processes occurring at the land surface, such as rainfall erosion, infiltration, ponding, and runoff. Conceptual and quantitative analyses reveal that scale differences may yield substantial biases. Even for perfect measurements, the overall bias is composed of two contributing factors: one related to a reduction of dynamic range of rain rates and the other related to a dependence of the relationship between observed radar reflectivity factor and retrieved rainfall rate on the scale of observation. The effects of scale differences are evaluated empirically from a perspective of averaging in time based on raindrop spectra observations. Averaging drop spectra over 5 min, on average over a large dataset, resulted in an underestimation of median and maximum rainfall rates of approximately 50% compared to the corresponding 1-min values. Overall, standard deviations of rain rates retrieved from 5-min-averaged radar reflectivity factors may easily be off a corresponding high-resolution (1 min) rainfall rate by a factor 2 or more. This magnitude is larger than the uncertainty resulting from limitations of the radar measurement precision. Scale-difference effects are thus important and should be considered when comparing, merging, or assimilating data from very different spatial and temporal scales. A similar challenge arises for downscaling schemes attempting to recover subgrid-scale features from coarse-resolution information. [ABSTRACT FROM AUTHOR]

The Z-R relationship is a scaling-law formulation, Z 5 ARb, connecting the radar reflectivity Z to the rain rate R. However, more than 100 Z-R relationships, with different values of the parameters, have been reported in literature. This abundance of relationships is in itself a strong indication that no one "physical" relationship exists, a state of affairs that we find similar to that of the protagonist of Luigi Pirandello's novel One, No One and One Hundred Thousand. Nevertheless the "elevation" of a simple linear fit in the (logR, logZ) space to the role of "scaling law" is such a widespread tenet in literature that it eclipses the simple realization that the abundance of different intercepts and slopes reflects the inhomogeneous nature of rain, and, in ultimate analysis, the statistical variability existing between the number of drops and drop size distribution. Here, we "eliminate" the contribution of the number of drops by rescaling both reflectivity and rainfall rate to per unit drop variables, (Z, R) / (z, r), so that the remaining variability is due only to the variability of the drop size distribution. We use a worldwide database of disdrometer data to show that for the rescaled variables (z, r) only "one," albeit approximate, scaling law exists. [ABSTRACT FROM AUTHOR]

This paper describes a neural network–based approach to estimate the conditional distribution function (cdf) of rainfall with respect to multidimensional satellite-derived input data. The methodology [Conditional Histogram of Precipitation (CHIP)] employs a histogram-based approximation of the cdf. In addition to the conditional expected rainfall rate, it provides conditional probabilities for that rate falling within each of a fixed set of intervals or bins. A test algorithm based on the CHIP approach was calibrated against Goddard profiling algorithm (GPROF) rainfall data for June–August 2002 and then used to produce a 30-min, 0.5° rainfall product from global (60°N–60°S) composite geostationary thermal infrared imagery for June–August 2003. Estimated rainfall rates and conditional probabilities were validated against 2003 GPROF data. The CHIP methodology provides the means to extend existing probabilistic and ensemble satellite rainfall error models, conditioned on a single, scalar, satellite rainfall predictor or upon scalar rainfall-rate outputs, to make full use of multidimensional input data. [ABSTRACT FROM AUTHOR]

Three satellite gridded daily precipitation datasets—PERSIANN-CDR, GPCP, and CMORPH—that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20 years to over 35 years. The products intercomparisons are performed at various temporal (daily to annual) resolutions and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in situ datasets from the Global Historical Climatology Network (GHCN-Daily) and the U.S. Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio, among others). All SPPs were found to underestimate extreme rainfall (i.e., above the 90th percentile) from about −20% for CMORPH to −50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned, from the long-term qualitative analysis of hydroclimatological properties to the quantification of daily extreme events, for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications. [ABSTRACT FROM AUTHOR]

Recent upgrades to operational radar-rainfall products in terms of quality and resolution call for reexamination of the factors that contribute to the uncertainty of radar-rainfall estimation. Remapping or regridding of radar observations onto Cartesian coordinates is implemented by practitioners when radar estimates are compared against rain gauge observations, in hydrologic applications, or for merging data from different radars. However, assuming perfect radar observations, many of the widely used remapping methodologies do not conserve mass for the rainfall rate field. The most popular remapping approaches used are those based on extracting information from radar bins whose centers fall within a certain distance from the center of the Cartesian grid. This paper develops a mass-conserving method for remapping, which is called 'precise remapping,' which is compared against two other commonly used remapping methods. Results show that the choice of the remapping method can make a substantial difference in grid-averaged rainfall accumulations (up to more than 100%). Differences were quantified using observations from two radars, collected during a field experiment. The interpolation grid resolution was also found to affect interpolated rainfall estimates. Approximate remapping methods tend to be much more sensitive to the interpolation grid resolution than precise remapping. High-resolution radar data such as those from radars with short gate spacing or narrow beams, or the super-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) sampling format, are significantly more sensitive (by up to 100%) to the remapping method and the interpolation grid resolution than the legacy WSR-88D rainfall data resolution of 1° × 1 km. [ABSTRACT FROM AUTHOR]

Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory's X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data. [ABSTRACT FROM AUTHOR]

An initial ground validation of the Integrated Multisatellite Retrievals for GPM (IMERG) Day-1 product from March 2014 to August 2015 is presented for the tropical Andes. IMERG was evaluated along with the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) against 302 quality-controlled rain gauges across Ecuador and Peru. Detection, quantitative estimation statistics, and probability distribution functions are calculated at different spatial (0.1°, 0.25°) and temporal (1 h, 3 h, daily) scales. Precipitation products are analyzed for hydrometeorologically distinct subregions. Results show that IMERG has a superior detection and quantitative rainfall intensity estimation ability than TMPA, particularly in the high Andes. Despite slightly weaker agreement of mean rainfall fields, IMERG shows better characterization of gauge observations when separating rainfall detection and rainfall rate estimation. At corresponding space-time scales, IMERG shows better estimation of gauge rainfall probability distributions than TMPA. However, IMERG shows no improvement in both rainfall detection and rainfall rate estimation along the dry Peruvian coastline, where major random and systematic errors persist. Further research is required to identify which rainfall intensities are missed or falsely detected and how errors can be attributed to specific satellite sensor retrievals. The satellite-gauge difference was associated with the point-area difference in spatial support between gauges and satellite precipitation products, particularly in areas with low and irregular gauge network coverage. Future satellite-gauge evaluations need to identify such locations and investigate more closely interpixel point-area differences before attributing uncertainties to satellite products. [ABSTRACT FROM AUTHOR]

Accurate estimation of precipitation at high spatial and temporal resolution of weather radars is an open problem in hydrometeorological applications. The use of dual polarization gives the advantage of multiparameter measurements using orthogonal polarization states. These measurements carry significant information, useful for estimating rain-path signal attenuation, drop size distribution (DSD), and rainfall rate. This study evaluates a new self-consistent with optimal parameterization attenuation correction and rain microphysics estimation algorithm (named SCOP-ME). Long-term X-band dual-polarization measurements and disdrometer DSD parameter data, acquired in Athens, Greece, have been used to quantitatively and qualitatively compare SCOP-ME retrievals of median volume diameter D0 and intercept parameter NW with two existing rain microphysical estimation algorithms and the SCOP-ME retrievals of rain rate with three available radar rainfall estimation algorithms. Error statistics for rain rate estimation, in terms of relative mean and root-mean-square error and efficiency, show that the SCOP-ME has low relative error if compared to the other three methods, which systematically underestimate rainfall. The SCOP-ME rain microphysics algorithm also shows a lower relative error statistic when compared to the other two microphysical algorithms. However, measurement noise or other signal degradation effects can significantly affect the estimation of the DSD intercept parameter from the three different algorithms used in this study. Rainfall rate estimates with SCOP-ME mostly depend on the median volume diameter, which is estimated much more efficiently than the intercept parameter. Comparisons based on the long-term dataset are relatively insensitive to path-integrated attenuation variability and rainfall rates, providing relatively accurate retrievals of the DSD parameters when compared to the other two algorithms. [ABSTRACT FROM AUTHOR]

Extreme rainfall from extratropical cyclones and the distinctive hydrology of the winter season both contribute to flood extremes in the Mid-Atlantic region. In this study, we examine extreme rainfall and flooding from a winter season extratropical cyclone that passed through the eastern United States on 24/25 February 2016. Extreme rainfall rates during the 24/25 February 2016 time period were produced by supercell thunderstorms; we identify supercells through local maxima in azimuthal shear fields computed from Doppler velocity measurements from WSR-88D radars. Rainfall rates approaching 250 mm h−1 from a long-lived supercell in New Jersey were measured by a Parsivel disdrometer. A distinctive element of the storm environment for the 24/25 February 2016 storm was elevated values of convective available potential energy (CAPE). We also examine the climatology of atmospheric rivers (ARs)—like the February 2016 storm—based on an identification and tracking algorithm that uses Twentieth Century Reanalysis fields for the 66-yr period from 1950 to 2015. Climatological analyses suggest that AR frequency is increasing over the Mid-Atlantic region. An increase in AR frequency, combined with increasing frequency of elevated CAPE during the winter season over the Mid-Atlantic region, could result in striking changes to the climatology of extreme floods. [ABSTRACT FROM AUTHOR]

Potentially, the greatest benefit of commercial microwave links (CMLs) as opportunistic rainfall sensors lies in regions that lack dedicated rainfall sensors, most notably low- and middle-income countries. However, current CML rainfall retrieval algorithms are predominantly tuned and applied to (European) CML networks in temperate or Mediterranean climates. This study investigates whether local quantitative precipitation estimates from CMLs in a tropical region, specifically Sri Lanka, can be improved by optimizing two dominant parameters in the rainfall retrieval algorithm RAINLINK, namely, the wet antenna attenuation correction factor Aa and the relative contribution of minimum and maximum received signal levels α. Using a grid search, based on 10 months of CML data from 22 link–gauge clusters consisting of 105 sublinks that lie within 1 km of a daily rain gauge, the optimal values of Aa and α are first derived for the entire country and compared to the default RAINLINK values. Subsequently, the CMLs are grouped by link length, frequency, climate zone, and daily rainfall depth classes, and Aa and α are derived for each of these classes. Calibrating parameters on all clusters across the country only leads to minor improvements. The actual optimal Aa and α values depend on the performance metric favored. Calibrating on network properties, particularly short link length and high-frequency classes, does significantly improve rainfall estimates. By relating the optimal Aa and α values to known network metadata, the results from this study are potentially applicable to other tropical CML networks that lack nearby reference rainfall data. Significance Statement: The purpose of this study is to improve rainfall estimates from commercial microwave links in Sri Lanka by optimizing two important rainfall retrieval algorithm parameters. Our results show that relating the optimal parameter values to operating frequency and pathlength improves rainfall estimates more than applying a single optimal parameter set to the entire network. By relating the optimal parameter values to readily known network properties, we aim to make these results applicable to other tropical countries, particularly low- and middle-income countries, that lack adequate reference rainfall data to calibrate rainfall estimates from commercial microwave links on. [ABSTRACT FROM AUTHOR]

The difficulty of representing high rainfall variability over mountainous areas using ground-based sensors is an open problem in hydrometeorology. Observations from locally deployed dual-polarization X-band radar have the advantage of providing multiparameter measurements near ground that carry significant information useful for estimating drop size distribution (DSD) and surface rainfall rate. Although these measurements are at fine spatiotemporal scale and are less inhibited by complex topography than operational radar network observations, uncertainties in their estimates necessitate error characterization based upon in situ measurements. During November 2015–February 2016, a dual-polarized Doppler on Wheels (DOW) X-band radar was deployed on the Olympic Peninsula of Washington State as part of NASA's Olympic Mountain Experiment (OLYMPEX). In this study, rain gauges and disdrometers from a dense network positioned within 40 km of DOW are used to evaluate the self-consistency and accuracy of the attenuation and brightband/vertical profile corrections, and rain microphysics estimation by SCOP-ME, an algorithm that uses optimal parameterization and best-fitted functions of specific attenuation coefficients and DSD parameters with radar polarimetric measurements. In addition, the SCOP-ME precipitation microphysical retrievals of median volume diameter D0 and normalized intercept parameter NW are evaluated against corresponding parameters derived from the in situ disdrometer spectra observations. [ABSTRACT FROM AUTHOR]

Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5° × 5° over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used to represent the joint probability distribution of satellite (PERSIANN) and radar (stage IV) rainfall. The method is further investigated for the period of 2006-08 over the Illinois watershed south of Siloam Springs, Arkansas. Results show that, using the proposed method, the estimation of the precipitation is improved in terms of percent bias and root-mean-square error. [ABSTRACT FROM AUTHOR]

Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O'ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O'ahu to date. Results show that δ18O and δ2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka'ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog's role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions. [ABSTRACT FROM AUTHOR]

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall-rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission. [ABSTRACT FROM AUTHOR]

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km2 grid cell experiences precipitation >15 mm h−1 for 1-h events or 30 mm day−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km2. In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km2) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation. [ABSTRACT FROM AUTHOR]

The structure and evolution of flash flood-producing storms over a small urban watershed in the mid-Atlantic United States with a prototypical flash flood response is examined. Lagrangian storm properties are investigated through analyses of the 32 storms that produced the largest peak discharges in Moores Run between January 2000 and May 2014. The Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm is used to track storm characteristics over their life cycle with a focus on storm size, movement, intensity, and location. First, the 13 June 2003 and 1 June 2006 storms, which produced the two largest peak discharges for the study period, are analyzed. Heavy rainfall for the 13 June 2003 and 1 June 2006 storms were caused by a collapsing thunderstorm cell and a slow-moving, low-echo centroid storm. Analyses of the 32 storms show that collapsing storm cells play an important role in peak rainfall rate production and flash flooding. Storm motion is predominantly southwest-to-northeast, and approximately half of the storms exhibited some linear organization. Mean storm total rainfall for the 32 storms displayed an asymmetric distribution around Moores Run, with sharply decreasing gradients southwest of the watershed (upwind and into the city) and increased rainfall to the northeast (downwind and away from the city). Results indicate urban modification of rainfall in flash flood-producing storms. There was no evidence that the storms split around Baltimore. Flood-producing rainfall was highly concentrated in time; on average, approximately 21% of the storm total rainfall fell within 15 min. [ABSTRACT FROM AUTHOR]

The quantitative estimation of rain rates using meteorological radar has been a major theme in radar meteorology and radar hydrology. The increase of interest in polarimetric radar is in part because polarization diversity can reduce the effect on radar precipitation estimates caused by raindrop size variability, which has allowed progress on radar rainfall estimation and on hydrometeorological applications. From an operational point of view, the promises regarding the improvement of radar rainfall accuracy have not yet been completely proven. The main reason behind these limits is the geometry of radar measurements combined with the variability of the spatial structure of the precipitation systems. To overcome these difficulties, a methodology has been developed to transform the estimated drop size distribution (DSD) provided by a vertically pointing micro rain radar to a profile given by a ground-based polarimetric radar. As a result, the rainfall rate at the ground is fixed at all ranges, whereas the broadening beam encompasses a large variability of DSDs. The resulting DSD profile is used to simulate the corresponding profile of radar measurements at C band. Rainfall algorithms based on polarimetric radar measurements were taken into account to estimate the rainfall into the radar beam. Finally, merit factors were used to achieve a quantitative analysis of the performance of the rainfall algorithm in comparison with the corresponding measurements at the ground obtained from a 2D video disdrometer (2DVD) that was positioned beside the micro rain radar. In this method, the behavior change of the merit factors in the range is directly attributable to the DSD variability inside the radar measurement volume, thus providing an assessment of the effects due to beam broadening. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission 3B42 precipitation estimates are widely used in tropical regions for hydrometeorological research. Recently, version 7 of the product was released. Major revisions to the algorithm involve the radar reflectivity-rainfall rate relationship, surface clutter detection over high terrain, a new reference database for the passive microwave algorithm, and a higher-quality gauge analysis product for monthly bias correction. To assess the impacts of the improved algorithm, the authors compare the version 7 and the older version 6 products with data from 263 rain gauges in and around the northern Peruvian Andes. The region covers humid tropical rain forest, tropical mountains, and arid-to-humid coastal plains. The authors find that the version 7 product has a significantly lower bias and an improved representation of the rainfall distribution. They further evaluated the performance of the version 6 and 7 products as forcing data for hydrological modeling by comparing the simulated and observed daily streamflow in nine nested Amazon River basins. The authors find that the improvement in the precipitation estimation algorithm translates to an increase in the model Nash-Sutcliffe efficiency and a reduction in the relative bias between the observed and simulated flows by 30%-95%. [ABSTRACT FROM AUTHOR]

The raindrop size distribution (RDSD) is defined as the relative frequency of raindrops per given diameter in a volume. This paper describes a mathematically consistent modeling of the RDSD drawing on probability theory. It is shown that this approach is simpler than the use of empirical fits and that it provides a more consistent procedure to estimate the rainfall rate ( R) from reflectivity ( Z) measurements without resorting to statistical regressions between both parameters. If the gamma distribution form is selected, the modeling expresses the integral parameters Z and R in terms of only the total number of drops per volume ( N T), the sample mean [ m = E( D)], and the sample variance [ σ2 = E( m − D)2] of the drop diameters ( D) or, alternatively, in terms of N T, E( D), and E[log( D)]. Statistical analyses indicate that ( N T, m) are independent, as are ( N T, σ2). The Z- R relationship that arises from this model is a linear R = T × Z expression (or Z = T−1 R), with T a factor depending on m and σ2 only and thus independent of N T. The Z- R so described is instantaneous, in contrast with the operational calculation of the RDSD in radar meteorology, where the Z- R arises from a regression line over a usually large number of measurements. The probabilistic approach eliminates the need of intercept parameters N0 or , which are often used in statistical approaches but lack physical meaning. The modeling presented here preserves a well-defined and consistent set of units across all the equations, also taking into account the effects of RDSD truncation. It is also shown that the rain microphysical processes such as coalescence, breakup, or evaporation can then be easily described in terms of two parameters-the sample mean and the sample variance-and that each of those processes have a straightforward translation in changes of the instantaneous Z- R relationship. [ABSTRACT FROM AUTHOR]

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the relative error structure of Tropical Rainfall Measurement Mission (TRMM) precipitation radar (PR) quantitative precipitation estimation (QPE) at the ground by comparison of 2A25 products with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25, version 7 (V7), products that were recently released as a replacement of version 6 (V6). Moreover, the authors supply uncertainty estimates of the rainfall products so that they may be used in a quantitative manner for applications like hydrologic modeling. This new version is considered superior over land areas and will likely be the final version for TRMM PR rainfall estimates. Several aspects of the two versions are compared and quantified, including rainfall rate distributions, systematic biases, and random errors. All analyses indicate that V7 is in closer agreement with the reference rainfall compared to V6. [ABSTRACT FROM AUTHOR]

The paper presents a rainfall estimation technique based on algorithms that couple, along a radar ray, profiles of horizontal polarization reflectivity (Z[sub H] ), differential reflectivity (Z[sub DR] ), and differential propagation phase shift (Φ[sub DP] ) from X-band polarimetric radar measurements. Based on in situ raindrop size distribution (DSD) data and using a three-parameter “normalized” gamma DSD model, relationships are derived that correct X-band reflectivity profiles for specific and differential attenuation, while simultaneously retrieving variations of the normalized intercept DSD parameter (N[sub w] ). The algorithm employs an iterative scheme to intrinsically account for raindrop oblateness variations from equilibrium condition. The study is facilitated from a field experiment conducted in the period October–November 2001 in Iowa City, Iowa, where observations from X-band dual-polarization mobile radar (XPOL) were collected simultaneously with high-resolution in situ disdrometer and rain-gauge rainfall measurements. The observed rainfall events ranged in intensity from moderate stratiform precipitation to high-intensity (>50 mm h[sup -1] ) convective rain cells. The XPOL measurements were tested for calibration, noise, and physical consistency using corresponding radar parameters derived from coincidentally measured raindrop spectra. Retrievals of N[sub w] from the attenuation correction scheme are shown to be unbiased and consistent with N[sub w] values calculated from independent raindrop spectra. The attenuation correction based only on profiles of reflectivity measurements is shown to diverge significantly from the corresponding polarimetric-based corrections. Several rain retrieval algorithms were investigated using matched pairs of instantaneous high-resolution XPOL observations with rain rates from 3-min-averaged raindrop spectra at close range (∼5 km) and rain-gauge measurements from further ranges (∼10 km). It is shown that combining along-a-ray (corrected Z[sub H] , Z[sub DR] , and specific differential phase shift) values gets the best performance in rainfall estimation with about 40% (53%) relative standard deviation in the radar–disdrometer (radar–gauge) differences. The case-tuned reflectivity–rainfall rate (Z–R) relationship gives about 65% (73%) relative standard deviation for the same differences. The systematic error is shown to be low (∼3% overestimation) and nearly independent of rainfall intensity for the multiparameter algorithm, while for the standard Z–R it varied from 10% underestimation to 3% overestimation. [ABSTRACT FROM AUTHOR]

Supercell thunderstorms, the storm systems responsible for most tornadoes, have often been dismissed as flood hazards. The role of supercell thunderstorms as flood agents is examined through analyses of storm systems that occurred in Texas (5–6 May 1995), Florida (26 March 1992), Nebraska (20–21 June 1996), and Pennsylvania (18–19 July 1996). Particular attention is given to the “Dallas Supercell,” which resulted in 16 deaths from flash flooding and more than $1 billion in property damage during the evening of 5 May 1995. Rainfall analyses using Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity observations and special mesonet rain gauge observations from Dallas, Texas, show that catastrophic flash flooding resulted from exceptional rainfall rates at 5–60-min timescales. The spatial structure of extreme rainfall was linked to supercell structure and motion. The “Orlando Supercell” produced extreme rainfall rates (greater than 300 mm h[sup -1] ) at 1–5-min timescales over a dense rain gauge network. The Nebraska and Pennsylvania storm systems produced record flooding over larger spatial scales than the Texas and Florida storms, by virtue of organization and motion of multiple storms over the same region. For both the Nebraska and Pennsylvania storms, extreme rainfall and tornadoes occurred in tandem. Severe rainfall measurement problems arise for supercell thunderstorms, both from conventional gauge networks and weather radar. It is hypothesized that supercell storms play a significant role in the “climatology” of extreme rainfall rates (100-yr return interval and greater) at short time intervals (1–60 min) in much of the central and eastern United States. [ABSTRACT FROM AUTHOR]

Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r2 value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition. [ABSTRACT FROM AUTHOR]

This article explores the application of thermodynamic perturbations to a historical midlatitude, wintertime, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14-km variable-resolution simulations completed with the U.S. Department of Energy's global Energy Exascale Earth System Model (E3SM). We show that the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under preindustrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks from around +1 to +2 K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential nonlinearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB. [ABSTRACT FROM AUTHOR]

High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG. [ABSTRACT FROM AUTHOR]

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [ABSTRACT FROM AUTHOR]

Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth's hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net-like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net-like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net-like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs' importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology. [ABSTRACT FROM AUTHOR]

We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities. Significance Statement: In this study, we investigate the possibility of deriving accurate high-resolution rainfall maps from commercial microwave link (CML) data in West Africa. The main challenges are the lack of reference data in this area and the adoption of existing processing tools without reference data. We show CML rainfall maps for Ouagadougou, Burkina Faso, with a resolution of 5 min and 0.55 km, which is unprecedented in this region. The comparison with the only available rain gauge, which provides data only at a daily resolution, yields a Pearson correlation of >0.95. An analysis of synthetically thinned-out networks shows that this accuracy is valid for the whole domain. Comparing reanalysis and satellite data with the rain gauge and CML data showed a poor performance of these gridded reference datasets. Also, a high coincidence of temporal dynamics between CML rainfall maps and satellite products was observed. Overall, these findings support the potential of CMLs for future hydrometeorological applications in West Africa. [ABSTRACT FROM AUTHOR]

Soil moisture--precipitation (SM-PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s-1), high convective available potential energy (>1500 J kg-1), and low convective inhibition (<250 J kg-1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM--PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM-PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions. [ABSTRACT FROM AUTHOR]

Three satellite precipitation datasets--CMORPH, PERSIANN-CDR, and GPCP--from the NOAA/ Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007-18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products' (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling--Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance--smaller bias, higher correlation, and a better KGE score--than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH's performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 08C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products. [ABSTRACT FROM AUTHOR]

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases. [ABSTRACT FROM AUTHOR]

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas. [ABSTRACT FROM AUTHOR]

Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application. [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]

In previous work it was found that over a small network of disdrometers, the variability of probability size distributions (PSDs) expressed using the relative dispersion (RD; the ratio of the standard deviation to the mean) increased with the expansion of the network size. The explanation is that the network acts to integrate the Fourier transform of the spatial correlation function from smallest wavelengths to those comparable to the network size . Consequently, as increases, so do the variances at the different drop sizes. Thus, RD and PSD variability grow as increases. The limits to this growth, however, were not determined quantitatively. This finding is given fuller theoretical quantitative meaning over much larger dimensions by explicitly deriving the variance contributions at all the different drop sizes as well as for a variety of moments of the PSD by using spatial radial correlation functions estimated from temporal correlations. This is justifiable when the time for each observation is short. One example is provided. The relative dispersion of the PSD is dominated by fluctuations in the occurrences of the larger drops. The RDs of the raw moments are only a few percent of the PSD. Thus, approaches attempting to estimate radial correlation functions using, say, radar measurements of moments are of limited utility, a usefulness further compromised by the distortion of the correlation function by filtering over the beam dimension. These findings present a challenge for efforts to validate remote sensing measurements by ground truth experiments using networks. [ABSTRACT FROM AUTHOR]

A 2D video disdrometer (2DVD) probe was used to gather detailed drop measurements over a 770-min rain event. Accumulated totals of the rainfall and of the number of drops for each square centimeter showed persistent, significant correlated structures across the approximately 11 cm × 11 cm grid of the 2DVD. This is surprising because larger-scale studies suggest that the values in each square centimeter should be highly correlated with very little variation. Nevertheless, this correlation remains strikingly similar to what is observed at a coarser resolution, suggesting that it somehow scales with spatial resolution. However, because the correlation functions are not power laws, the origin of this scaling must be due to a factor other than fractal geometry. Analysis reveals that this occurs because of a filtering effect such that as the domain size (or resolution of a remote sensor) becomes finer, it is only the smaller wavelengths that contribute most to the variance so that the correlation function also scales. Consequently, correlated finescale structures can apparently occur even over 10 cm. This fine structure was also found for the kinetic energy and impact power of the rain, important for understanding the initiation of soil erosion. The patterns in the integrated parameters appeared to arise almost exclusively from patterns in the total number of drops with patterns in the drop sizes playing an insignificant role. Therefore, in future studies of rain it is recommended that the total number of drops be retained as a crucial variable. [ABSTRACT FROM AUTHOR]

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS). [ABSTRACT FROM AUTHOR]

The NEXRAD program has recently upgraded the WSR-88D network observational capability with dual polarization (DP). In this study, DP quantitative precipitation estimates (QPEs) provided by the current version of the NWS system are evaluated using a dense rain gauge network and two other single-polarization (SP) rainfall products. The analyses are performed for the period and spatial domain of the Iowa Flood Studies (IFloodS) campaign. It is demonstrated that the current version (2014) of QPE from DP is not superior to that from SP mainly because DP QPE equations introduce larger bias than the conventional rainfall-reflectivity [i.e., R( Z)] relationship for some hydrometeor types. Moreover, since the QPE algorithm is based on hydrometeor type, abrupt transitions in the phase of hydrometeors introduce errors in QPE with surprising variation in space that cannot be easily corrected using rain gauge data. In addition, the propagation of QPE uncertainties across multiple hydrological scales is investigated using a diagnostic framework. The proposed method allows us to quantify QPE uncertainties at hydrologically relevant scales and provides information for the evaluation of hydrological studies forced by these rainfall datasets. [ABSTRACT FROM AUTHOR]

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are examined over three coastal regions of the United States: the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes. [ABSTRACT FROM AUTHOR]