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The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements. [ABSTRACT FROM AUTHOR]

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non‐linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one‐dimensional physics‐based model of the near‐surface unsaturated zone to compare the effects of different rainfall discretization (5‐min point‐scale; hourly point‐scale; hourly 0.125° gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam) were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non‐negligible, with the hourly point‐scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5‐min rainfall. Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine‐grained soils. The results suggest that "hyperresolution" modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub‐grid precipitation parameterizations. [ABSTRACT FROM AUTHOR]

Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p > 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals. Significance Statement: Engineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the "2-yr storm" to the "100-yr storm"). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency. [ABSTRACT FROM AUTHOR]

Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer. [ABSTRACT FROM AUTHOR]

Despite the importance of soil moisture (SM) in various applications and the need to validate satellite SM products, the current in situ SM network is still inadequate, even for developed country such as the United States. Recently, SM2RAIN (Soil Moisture to Rain) algorithm has prominently emerged as a bottom-up approach to derive rainfall data from SM. In this study, we evaluated whether SM2RAIN algorithm and rain gauges, which are more abundant and readily available than in situ SM, can be used to validate satellite-based SMAP SM estimates. Since errors in SMAP SM propagate to SMAP-derived rainfall, the skills of SM2RAIN might be able to provide insights on the accuracy of SMAP SM observations. While the correlation between SM2RAIN skills and SMAP SM skills was found to be statistically significant, the strength of the correlation varied among different climate zones and annual rainfall classes. Specifically, weaker correlations were observed in arid and lower rainfall regions (median R value of 0.12), while stronger correlations were found in temperate and higher rainfall regions (median R value of 0.54). In term of over/under-estimation tendencies, 56% of the stations had the same tendencies (SM2RAIN skills and satellite SM skills both have positive or negative PBIAS value). [ABSTRACT FROM AUTHOR]

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models. Plain Language Summary: This paper focuses on the observational analysis of how soil moisture (SM) influences the vertical cloud‐water distribution throughout the day. By analyzing data from Soil Moisture Active Passive (SMAP) and Dual‐frequency Precipitation Radar (DPR), we gain insights into how antecedent SM affects cloud‐water reflectivity at different heights in the lower atmosphere. Our data‐driven approach produces spatial maps of SM's contribution to cloud reflectivity and rainfall in the central US as a function of cloud height and SM time lag. Our method will help diagnose weather and climate model biases. Key Points: Functional decomposition of satellite‐measured soil moisture (SM) and cloud vertical profiles (CVP) provides insights into SM‐CVP feedbacksThe sign and strength of this feedback varies with height, time lag, and geographic location, in agreement with qualitative studiesOur approach can be used to diagnose weather and climate model biases as they relate to land‐atmosphere coupling [ABSTRACT FROM AUTHOR]

Total rainfall accumulation, as well as convective and stratiform rainfall rate data from the Tropical Rainfall Measuring Mission (TRMM) satellite sensors have been used to derive the thunderstorm ratio and one-minute rainfall rates, R0.01, for 57 stations in Malaysia for exceedance probabilities of 0.001-1% for an average year, for the period 1998-2010. The results of the rain accumulations from the TRMM satellite were validated with the data collected from different ground data sources from the National Oceanic and Atmospheric Administration (NOAA) global summary of the day (1949-2010), Global Precipitation Climatology Centre (GPCC) (1986-2010), and NASA (1950-1999). The correlation coefficient and the average bias error between TRMM and GPCC for Malaysia were found to be 0.79-0.89 and ±50 mm, respectively. The deduced one-minute rainfall rates correlated fairly well with those obtained from the previous work carried out in Malaysia, with correlation coefficients of 0.7 in all the 57 locations. The inferred mean annual one-minute rainfall rates were found to be highest in the eastern Malaysia, with values between 84.7 and 153.9 mmh-1 for 0.01% exceedance, and in western Malaysia with values between 81.8 and 143.8 mmh-1. The present results will be useful for satellite rain attenuation modeling in tropical and subtropical stations around the world. [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]

A method to retrieve total vertical amounts of cloud liquid and ice in stratiform precipitating systems is described. The retrievals use measurements from the vertically pointing Ka- and W-band cloud radars operated by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and auxiliary measurements from a scanning National Weather Service radar and a ground-based disdrometer. Separation between the cloud liquid and rain is based on estimations of the total attenuation of millimeter-wavelength radar signals in the liquid hydrometeor layer. Disdrometer measurements are used for the retrieval constraints. Because the liquid phase hydrometeor retrievals use only differential measurements, they are immune to the absolute radar calibration uncertainties. Estimates of the ice cloud phase are performed using empirical relations between absolute radar reflectivity and ice water content. Data from the nearby scanning weather-service radar, which operates at a lower frequency, are used to correct cloud radar measurements observed above the freezing level for attenuation caused by the layers of liquid and melting hydrometeors and also by wet radomes of cloud radars. Polarimetric and vertical Doppler measurements from ARM cloud radars provide a distinct separation between regions of liquid and ice phases, and therefore the corresponding retrievals are performed in each region separately. The applicability of the suggested method is illustrated for a stratiform precipitation event observed at the ARM Southern Great Plains facility. Expected uncertainties for retrievals of cloud liquid water path are estimated at about 200–250 g m-2 for typical rainfall rates observed in stratiform systems (∼3–4 mm h-1). These uncertainties increase as rainfall rate increases. The ice water path retrieval uncertainties can be as high as a factor of 2. [ABSTRACT FROM AUTHOR]

A description is given of rainfall characteristics associated with the remnants of Tropical Storm Helene, which made landfall on the panhandle of Florida and moved through parts of Georgia, South Carolina, and North Carolina during 22–23 September 2000. Also included in the description are rainfall events in advance of the approach of Helene to South Carolina. Emphasis is placed on the implications of these characteristics regarding the ability of the Weather Surveillance Radar-1988 Doppler (WSR-88D) to measure rainfall accurately for this system when employing reflectivity factor–rainfall rate (Z–R) algorithms as defined by the National Weather Service. The results are based on analysis of raindrop size spectra collected with a disdrometer in upstate South Carolina at the Clemson University Atmospheric Research Laboratory. It was found that there were pronounced variations in the Z–R relationship from day to day and within a day that would limit the accuracy of radar measurement of rainfall. A discussion is presented of the effects of these variations on the decisions made by meteorologists with regard to the choice of Z–R used during operation of the radar. [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 structural evolution of the inner core and near environment throughout the life cycle of Hurricane Edouard (2014) is examined using a synthesis of airborne and satellite measurements. This study specifically focuses on the precipitation evolution and thermodynamic changes that occur on the vortex scale during four periods: when Edouard was a slowly intensifying tropical storm, another while a rapidly intensifying hurricane, during the initial stages of weakening after reaching peak intensity, and later while experiencing moderate weakening in the midlatitudes. Results suggest that, in a shear-relative framework, a wavenumber-1 asymmetry exists whereby the downshear quadrants consistently exhibit the greatest precipitation coverage and highest relative humidity, while the upshear quadrants (especially upshear right) exhibit relatively less precipitation coverage and lower humidity, particularly in the midtroposphere. Whether dynamically or precipitation driven, the relatively dry layers upshear appear to be ubiquitously caused by subsidence. The precipitation and thermodynamic asymmetry is observed throughout the intensification and later weakening stages, while a consistently more symmetric distribution is only observed when Edouard reaches peak intensity. The precipitation distribution, which is also discussed in the context of the boundary layer thermodynamic properties, is intimately linked to the thermodynamic symmetry, which becomes greater as the frequency, areal coverage, and, in particular, rainfall rate increases upshear. Although shear is generally believed to be detrimental to intensification, observations in Edouard also indicate that subsidence warming from mesoscale downdrafts in the low- to midtroposphere very near the center may have contributed favorably to organization early in the intensification stage. [ABSTRACT FROM AUTHOR]

Hydrograph methods are used to identify functional dependencies of the rational-method runoff coefficient, and nested intensity-duration-frequency (IDF) rainfall distributions are used to evaluate the assumption that a uniform rainfall intensity with duration equal to the time of concentration leads to the maximum peak runoff rate. Key findings of this study are that the runoff coefficient can have a substantial dependence on both the IDF function and the time of concentration of the catchment, the runoff coefficient can take on values greater than one, and the peak runoff rate is underestimated by assuming a constant rainfall intensity of duration equal to the time of concentration. [ABSTRACT FROM AUTHOR]

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products. [ABSTRACT FROM AUTHOR]

A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The RSD retrieval algorithm utilizes polarimetric variables measured by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), through an ad hoc regularized neural network method. Evaluation of rainfall estimation from the NN-based method is accomplished using a large radar data and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign. Point estimates of hourly rainfall accumulations and instantaneous rainfall rates from NN-based and parametric polarimetric rainfall relations are compared with dense surface gauge observations. Rainfall accumulations from RSD retrieval-based methods are shown to be sensitive to the choice of a raindrop fall speed model. To minimize the impact of this choice, a new “direct” neural network approach is tested. Proposed NN-based approaches exhibit bias and root-mean-square error characteristics comparable with those obtained from parametric relations, specifically optimized for the JPOLE dataset, indicating an appealing generalization capability with respect to the climatological context. All tested polarimetric relations are shown to be sensitive to hail contamination as inferred from the results of automatic polarimetric echo classification and available storm reports. [ABSTRACT FROM AUTHOR]

Filtering of nonpredictable scales of precipitation can be used to improve forecast precision (rms). Previous papers have studied the scale dependence of predictability of patterns of instantaneous rainfall rate and of probabilistic forecasts. In this paper, motivated by the often localized, intermittent nature of rainfall, the wavelet transform is used to develop measures of predictability at each scale. These measures are then used to design optimal forecast filters. This method is applied to radar composites of rainfall reflectivity over much of the continental United States and is developed to be appropriate for operational forecasts of rainfall rates and raining areas. For the four precipitation events studied, the average correlation at 4-h lead time was increased from 0.50 for the original nowcasts to 0.62 with forecast filtering. This forecast filtering is incorporated into the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), which now includes variational echo tracking, a semi-Lagrangian advection scheme, scale-based filtering, and appropriate rescaling of the filtered nowcast fields. [ABSTRACT FROM AUTHOR]

Impacts of diurnal radiative forcing on flow and rainfall patterns during summer flood and drought conditions (1993 and 1988, respectively) in the central United States were investigated using a regional climate model. The modeling approach, which included evaluation of sensitivity to modification in the solar hour, enabled evaluation of the impact on an event-by-event basis. The effect of the solar hour forward shift of 12 h on boundary layer wind speed over north-central Texas, which is often related to rainfall in the central United States through northward moisture advection, followed almost exactly the shift in solar hour. Domain-averaged daily rainfall in the central United States simulated with 12-h solar shift frequently showed in the flood year a backward or forward time shift of ∼12 h in the timing of its peak, an increase or decrease of rainfall rate, and on a few occasions noticeable formation of short-lived rainfall events. This pattern suggests relatively high sensitivity to the timing of the diurnal radiative forcing with respect to the large-scale perturbations. In contrast, in the drought year 12-h solar shifted simulations these modifications were weaker. The climatological domain-average diurnal cycle of rainfall showed for the flood year a well-defined 12-h shift when comparing the control and perturbed simulations. In contrast, in the drought year such a shift was not well defined. [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]

Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the 'rock moisture' reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM, LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soils databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case-study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a mixed evergreen temperate broadleaf-needleleaf forest whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models. [ABSTRACT FROM AUTHOR]

In the western United States, snow accumulation, storage, and ablation affect seasonal runoff. Thus, the prediction of snowmelt is essential to improve the reliability of water supply forecasts to guide water allocation and operational decisions. The current method used at the Colorado Basin River Forecast Center (CBRFC) couples the SNOW-17 temperature-index snow model and the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model in a lumped approach. Limitations in parameter transferability and calibration requirements for changing conditions with the temperature-index model motivated this research, in which new avenues were investigated to assess and prototype the application of an energy-balance snow model in a distributed modeling approach. The Utah Energy Balance (UEB) model was chosen to compare with the SNOW-17 model because it is simple and parsimonious, making it suitable for distributed application with the potential to improve water supply forecasts. Each model was coupled with the SAC-SMA model and the Rutpix7 routing model to simulate basin snowmelt and discharge. All the models were applied on grids over watersheds using the Research Distributed Hydrologic Model (RDHM) framework. Case studies were implemented for two study sites in the Colorado River basin over a period of two decades. The model performance was evaluated by comparing the model output with observed daily discharge and snow-covered area data obtained from remote sensing sources. Simulated evaporative components of sublimation and evapotranspiration were also evaluated. The results showed that the UEB model, requiring calibration of only a snow drift factor, achieves a comparable performance to the calibrated SNOW-17 model, and both provided reasonable basin snow and discharge simulations in the two study sites. The UEB model had the additional advantage of being able to explicitly simulate sublimation for different land types and thus better quantify evaporative water balance components and their sensitivity to land cover change. UEB also has a better transferability potential because it requires calibration of fewer parameters than SNOW-17. The majority of the parameters for UEB are physically based and regarded as constants characterizing spatially invariant properties of snow processes. Thus, the model remains valid for different climate and terrain conditions for multiple watersheds. [ABSTRACT FROM AUTHOR]

Numerous studies have evaluated the reliability and hydrologic utility of various rainfall data sets through hydrological modeling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty‐eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall data sets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting model simulations. Such rainfall data sets with varied errors are used to independently calibrate a widely used conceptual Xin'anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high‐performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event‐based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality. Plain Language Summary: Accurate rainfall is an ideal input for the hydrological model to simulate streamflow reliably. However, some inaccurate rainfall data sets can be compensated by hydrological model calibration to generate good streamflow. It is still unclear what kind of rainfall inputs the model calibration can and cannot adapt to. Here, we explore a large number of rainfall data sets from different sources and their corresponding streamflow simulation performance after independent model calibrations. It is found that the hydrological model can adapt well to two scenarios of inaccurate rainfall data. The adaptive ability of the hydrological model calibration to rainfall inputs is not only affected by the accuracy over the entire period but also by the accuracy of individual rainfall events, especially those of the most severe rainstorms. Therefore, a good hydrological simulation does not always mean its rainfall input data are reliable. This study enhances a quantitative understanding of how the model calibration adapts to the errors in rainfall input data. The findings are expected to provide quantitative guidance for bias correction and data fusion of rainfall products. Key Points: Two scenarios of inaccurate rainfall inputs and a threshold of overall rainfall bias are identified for hydrological model adaptabilityHydrological model adaptability is further influenced by how event‐based bias of individual rainstorms shapes the overall rainfall biasHydrological model adaptability is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage [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]

In this study, we investigate both the changes of tropical cyclone (TC) rainfall hazard in the United States under climate change and the relative importance of the factors that cause the changes. We find that under the SSP5 8.5 scenario, the 100‐year TC rainfall level can increase by up to 320% along the U.S. coastline by the end of this century. The influence of TC rainfall‐producing ability increase is more significant than the influence of TC frequency increase on the increase of the 100‐year TC rainfall level (up to 180% vs. 60% increase). Among the different physical drivers for the increase in storm rainfall‐producing ability, the increase of TC intensity is the leading factor, followed by changes in TC duration and atmospheric temperature. The projected increase of TC rainfall hazard is robust against the uncertainty in the TC frequency projection. Plain Language Summary: Rainfall associated with landfalling tropical cyclones (TC) can cause extreme flooding and landslides, which are hazardous for coastal and inland areas. We project that by the end of the 21st century, TC rainfall hazard in the United States will increase by as much as 320% compared to its current value. The increase in TC rainfall hazard can be caused by two factors: more TCs making impact and TCs becoming more capable of producing rainfall in the future. The increased ability for TCs to produce rainfall is linked to increased TC intensity and duration as well as a warmer and more humid atmosphere in the future. Though uncertainties exist in the projected trend of future TC landfall frequency, TC rainfall hazard is very likely to increase due to the significant increase in TC rainfall‐producing ability. Key Points: U.S. landfalling tropical cyclone (TC) rainfall hazard will increase in the future due to increased TC landfalling frequency and TC rainfall‐producing abilityStorm intensity change is the leading factor for TC rainfall‐producing ability to increaseThe projected increase of TC rainfall hazard is robust against the uncertainty in TC frequency projection [ABSTRACT FROM AUTHOR]

Abstract: Turfgrass irrigation policies have been implemented in various regions of the U.S. to reduce domestic water consumption. Mandatory restrictions are often enforced by issuing citations to violators with the intent to promote compliance and deter violations. This study provides a detailed investigation of past water restrictions and compliance, and attempts to determine which factors are related to high rates of water usage within communities of Tampa, FL. The adjusted rainfall rate had the most significant relationship with water usage in the communities under study. Water usage increased in each examined community after it transitioned to more stringent water usage restrictions, with cited restriction violators increasing usage to a greater extent than their uncited counterparts. This increase may primarily be attributed to conflicts between the local water consumption policy and binding homeowner association rules. When the once-a-week usage restriction was in place, the area faced drought conditions. Therefore, homeowners irrigated more to meet the water needs of their lawns despite the restrictions imposed on them by their local government. [Copyright &y& Elsevier]

This study focuses on the evaluation of the NOAA-NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space-time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates >2 mm h−1 and probability of false detection <3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications. [ABSTRACT FROM AUTHOR]

The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km2. It consists of 88 weighing rain gauges within a 149-km2 area. On average, approximately 10 gauges can be found in each PR field of view (~5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m × 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999-2010). The results indicate very good agreement between the fields with low bias values (<10%) and high correlation coefficients, especially for the near-nadir cases (>0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products. [ABSTRACT FROM AUTHOR]

Flooding in the eastern United States reflects a mixture of flood-generating mechanisms, with landfalling tropical cyclones and extratropical systems playing central roles. The authors examine the climatology of heavy rainfall and flood magnitudes for the eastern United States through analyses of long-duration records of flood peaks and maximum daily rainfall series. Spatial heterogeneities in flood peak distributions due to orographic precipitation mechanisms in mountainous terrain, coastal circulations near land--ocean boundaries, and urbanization impacts on regional climate are central elements of flood peak distributions. Lagrangian analyses of rainfall distribution and storm evolution are presented for flood events in the eastern United States and used to motivate new directions for stochastic modeling of rainfall. Tropical cyclones are an important element of the upper tail of flood peak distributions throughout the eastern United States, but their relative importance varies widely, and abruptly, in space over the region. Nonstationarities and long-term persistence of flood peak and rainfall distributions are examined from the perspective of the impacts of human-induced climate change on flood-generating mechanisms. Analyses of flood frequency for the eastern United States, which are based on observations from a dense network of U.S. Geological Survey (USGS) stream gauging stations, provide insights into emerging problems in flood science. [ABSTRACT FROM AUTHOR]

Heavy precipitation in the northeastern United States is examined through observational and numerical modeling analyses for a weather system that produced extreme rainfall rates and urban flash flooding over the New York–New Jersey region on 4–5 October 2006. Hydrometeorological analyses combine observations from Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars, the National Lightning Detection Network, surface observing stations in the northeastern United States, a vertically pointing lidar system, and a Joss–Waldvogel disdrometer with simulations from the Weather Research and Forecasting Model (WRF). Rainfall analyses from the Hydro-Next Generation Weather Radar (NEXRAD) system, based on observations from WSR-88D radars in State College, Pennsylvania, and Fort Dix, New Jersey, and WRF model simulations show that heavy rainfall was organized into long-lived lines of convective precipitation, with associated regions of stratiform precipitation, that develop along a frontal zone. Structure and evolution of convective storm elements that produced extreme rainfall rates over the New York–New Jersey urban corridor were influenced by the complex terrain of the central Appalachians, the diurnal cycle of convection, and the history of convective evolution in the frontal zone. Extreme rainfall rates and flash flooding were produced by a “leading line–trailing stratiform” system that was rapidly dissipating as it passed over the New York–New Jersey region. Radar, disdrometer, and lidar observations are used in combination with model analyses to examine the dynamical and cloud microphysical processes that control the spatial and temporal structure of heavy rainfall. The study illustrates key elements of the spatial and temporal distribution of rainfall that can be used to characterize flash flood hazards in the urban corridor of the northeastern United States. [ABSTRACT FROM AUTHOR]

Abstract: Convective systems are tracked for two summer months over the continental USA and their properties at different stages of life are investigated in terms of Cloud to Ground (CG) lightning flashes, cloud kinematics, and precipitation. The study employs lightning data from the National Lightning Detection Network (NLDN) along with 1/2-hourly satellite Infrared observations, and hourly rainfall accumulation fields from the nationwide WSR-88D radar network. Results show strong dependence of the amount of CG lightning flashes with the stage of the storm''s life cycle. The highest number of CG lightning flashes and highest area expansion rates are also seen to be during the growth stage of the systems. The stronger the lightning density, or cloud top area expansion rate, of the storms at their initiation stage is the longer is shown to be their life duration. The average rainfall yield per flash in the growing, mature and dissipation stages of storms with duration greater than 4 h is 4.7, 12.5 and 8.5 107 kg, respectively. The temporal evolution of the storms'' area and CG flash rate are shown to correlate well with the evolution of rainfall parameters, such as storm''s rainfall yield (m3), conditional mean rainfall rate, and the fraction of rain area. The consideration of the kinematics and CG lightning flashes of convective systems and the knowledge of their life cycle stage demonstrate a potential for drawing information useful in rain rate retrievals and their variability. [Copyright &y& Elsevier]

Studies in the past several years have documented the climatology of warm-season precipitation-episode statistics (propagation speed, span, and duration) over the United States using a national composited radar dataset. These climatological studies have recently been extended to other continents, including Asia, Africa, and Australia. However, continental regions outside the United States have insufficient radar coverage, and the newer studies have had to rely on geostationary satellite data at infrared (IR) frequencies as a proxy for rainfall. It is well known that the use of IR brightness temperatures to infer rainfall is subject to large errors. In this study, the statistics of warm-season precipitation episodes derived from radar and satellite IR measurements over the United States are compared and biases introduced by the satellite data are evaluated. It is found that the satellite span and duration statistics are highly dependent upon the brightness temperature threshold used but with the appropriate choices of thresholds can be brought into good agreement with those based upon radar data. The propagation-speed statistics of satellite events are on average ∼4 m s-1 faster than radar events and are relatively insensitive to the brightness temperature threshold. A simple correction procedure based upon the difference between the steering winds for the precipitation core and the winds at the level of maximum anvil outflow is developed. [ABSTRACT FROM AUTHOR]

Hydrogeomorphology is an emerging discipline that studies the relationship between landforms and hydrology, focusing on groundwater and surface water interactions. This study presents the methodology for the elaboration of a hydro-geomorphological map oriented to illustrate the relationships between the aquifer components and geomorphological characteristics in the United States-Mexico Transboundary San Pedro Aquifer (TSPA). This information contributes to a further understanding of the TSPA, facilitates the location of groundwater recharge and discharge zones, is useful for the development of sustainable groundwater management strategies, and could be useful in developing conceptual and numerical groundwater models for the region. [ABSTRACT FROM AUTHOR]

Two years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts. Key Points: Several satellite‐based precipitation products are compared in relation with orographic enhancementSatellite‐based precipitation BIAS shows a seasonal patternPrecipitation BIAS is associated with the surface temperatures and total precipitable water [ABSTRACT FROM AUTHOR]

The western and central United States experience a pronounced diurnal cycle in rainfall during the warm season. Over the higher terrain west of 105°W, most precipitation occurs in the afternoon, whereas the central United States experiences more nocturnal events. This coherent phase transition between the Rocky Mountains and the U.S. Great Plains is well defined for all warm seasons between 1996 and 2003, provided that the rainfall observations are remapped relative to the elevated terrain in the western United States prior to north–south averaging. Due to the westward shift of the Continental Divide north of 42°N and its intersection with the warm season storm track for 2002, the diurnal coherence greatly improves after remapping the 2002 rainfall observations. This speaks to the long-range influence of orography on precipitation frequency and suggests that the primary east–west corridor of precipitation for an individual warm season intersects the cordillera over a fairly narrow latitude range. [ABSTRACT FROM AUTHOR]

The hydrometeorological processes that control flash flooding are examined through analyses of space–time rainfall variability and flood response in the Milwaukee metropolitan region. The analyses focus on four flood events in the Menomonee River basin that occurred 21 June 1997, 2 July 1997, 6 August 1998, and 21 July 1999. The June 1997 and August 1998 flood events produced record flood peaks in the Menomonee River and its tributaries. Rainfall analyses, which are based on WSR-88D radar reflectivity observations and rainfall measurements from a dense network of rain gauges maintained by the city of Milwaukee, provide rainfall fields for each event at 1-km spatial resolution and 5-min timescale. The June 1997 and August 1998 storms exhibited striking contrasts in storm structure, evolution, and motion. Analyses of the structure and evolution of these storms are presented in conjunction with scaling analyses of the rainfall fields. The contrasting storm-scale properties of the June 1997 and August 1998 events resulted in sharp contrasts in extreme flood response between the two events. The regional flood response of the Menomonee River basin is examined in terms of space–time rainfall variability and heterogeneous land surface properties. Analyses are based on radar rainfall fields and 15-min discharge observations from stream gauging stations, with drainage area ranging from 47 to 319 km[sup 2] for the four flood events. Extreme flood response is examined in terms of flood peak magnitudes, peak response times, and event water balance. A distributed hydrologic model, which includes a Hortonian infiltration model and a network-based representation of hillslope and channel response, plays a central role in examining the regional flood response. [ABSTRACT FROM AUTHOR]

A bias-adjusted radar rainfall product is created and used for evaluation of two satellite rainfall estimation algorithms. Three years of collocated rainfall estimates from radar, rain gauges, a microwave satellite algorithm, and a multispectral (visible through near-infrared) algorithm were collected over the continental United States from July 1998 through July 2001. The radar and gauge data are compared to determine the locations and times at which the rainfall occurrences estimated by these two sensors are in sufficient agreement for the data to be used for validation. This procedure serves as quality control for both sensors and determines the locations at which the radar has difficulty detecting rainfall and should not be used in a validation dataset. For the data remaining after quality control, the gauge data are used for multiplicative adjustment of the radar estimates to remove the radar bias with respect to the gauges. These bias-adjusted estimates are compared with the satellite rainfall estimates to observe the evolution of the satellite biases over the 3-yr period. The multispectral algorithm was under development throughout the 3-yr period, and improvement is evident. The microwave algorithm overestimates rainfall in the summer months, underestimates in the winter months, and has an east-to-west bias gradient, all of which are consistent with physical explanations and previous findings. The multispectral algorithm bias depends highly on diurnal sampling; there is much greater overestimation for the daytime overpasses. These results are applicable primarily to the eastern half of the United States, because few data in the western half remain after quality control. [ABSTRACT FROM AUTHOR]

Examines radar reflectivity, rain rate, and kinetic energy flux relationships based on raindrop spectra in Mississippi. Effectiveness of raindrop spectrometer in obtaining rainfall momentum flux; Description of raindrop size distribution of intense rainfall; Effect of sample size on rainfall parameters derived from drop spectra samples.

This note documents the effects of a radar calibration error that affected 5% of the data used in an earlier study by Carbone et al. While the original intent of this correction was merely to set the record straight, a significant finding emerged from the revised time series. The main impact is to reveal a more rapid spatial decorrelation between island Froude number (Fr) and oceanic rainfall, a measure of the dynamical blocking effects upstream of Hawaii. There no longer exists a residual correlation between Fr and cumulative rainfall over the remote ocean, defined in this instance as distances >50 km from shore. This result both simplifies and strengthens the conclusions of the original work, which was based on a very short time series. The strong dependence of rainfall on Fr over the coastal lowlands and near shore remains unchanged from the results presented by Carbone et al. To the extent that remote oceanic rainfall is expected to be uncorrelated with Fr, there is increased confidence with respect to the causes and effects of island blocking on rainfall production over and near the island. [ABSTRACT FROM AUTHOR]

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference "ground-truth" product ("effective resolution"). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively "smooth." The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products. Significance Statement: Precipitation estimation products are essential for understanding water cycle dynamics and climate change, and for decision support in regions lacking ground observations. Several global products exist from multiple satellites orbiting Earth, but the challenge remains that of evaluating these products for accuracy and for improving the retrieval algorithms. Here we posit that the classical "pixel-to-pixel" comparison is not adequate and propose an approach that focuses on comparing space–time dynamics through a Fourier spectral analysis, which provides information about the size, shape, and orientation of precipitation systems, as well as their motion speed and direction. We evaluate five state-of-the-art multisatellite products and identify shortcomings, in particular in their ability to capture the submesoscale variability of precipitation. [ABSTRACT FROM AUTHOR]

Despite the increased awareness of benefits and opportunities resulting from energy retrofits, there is a lack of information regarding the factors influencing the success of energy retrofits – especially deep energy retrofits – aiming to achieve a net zero energy goal. The aim of this study is to identify factors and variables that could contribute to the successful implementation of energy retrofit measures in buildings with the goal of achieving net zero energy. A database of 34 small- and medium-sized commercial building retrofit projects was developed for this analysis, which was derived from the New Buildings Institute's Net Zero Energy Building Database. Five factor categories were investigated: physical, technical, environmental, economic, and policy. A total of 11 variables from the 5 categories were analysed. The results show that success stems from a combination of certain variables: the number of floors, the climate zone, and the building cost. In general, the results indicate a trend: compact buildings with a construction cost range between $19/m2 and $42/m2 in a mild climate have a higher likelihood of achieving a net zero energy goal. Also, the analysis provides new evidence toward the importance of understanding a building's physical characteristics in an existing building energy retrofit. [ABSTRACT FROM AUTHOR]

By modulating the moisture flux from ocean to adjacent land, the North Atlantic subtropical high (NASH) western ridge significantly influences summer-season total precipitation over the conterminous United States (CONUS). However, its influence on the frequency and intensity of daily rainfall events over the CONUS remains unclear. Here we introduce a Bayesian statistical model to investigate the impacts of the NASH western ridge position on key statistics of daily scale summer precipitation, including the intensity of rainfall events, the probability of precipitation occurrence, and the probability of extreme values. These statistical quantities play a key role in characterizing both the impact of wet extremes (e.g., the probability of floods) and dry extremes. By applying this model to historical rain gauge records (1948–2019) covering the entire CONUS, we find that the western ridge of the NASH influences the frequency of rainfall as well as the distribution of rainfall intensities over extended areas of the CONUS. In particular, we find that the NASH ridge also modulates the frequency of extreme rainfall, especially that over part of the Southeast and Upper Midwest. Our analysis underlines the importance of including the NASH western ridge position as a predictor for key statistical rainfall properties to be used for hydrological applications. This result is especially relevant for projecting future changes in daily rainfall regimes over the CONUS based on the predicted strengthening of the NASH in a warming climate. Significance Statement: The purpose of this work is studying how the position of the North Atlantic subtropical high (NASH) western ridge modulates summer daily precipitation statistics over the conterminous United States (CONUS). We introduce a Bayesian statistical model describing daily precipitation frequency, intensity, and probability of extremes. We find that the NASH is an important predictor for daily rainfall statistics over large areas of the CONUS, in particular over the Southeast and Midwest. Since the NASH is predicted to strengthen in future climate conditions, our results are particularly relevant for understanding the corresponding shift in the probability distribution and occurrence of daily precipitation. [ABSTRACT FROM AUTHOR]

Small watersheds are ideal objects for studying the evolution of hydrological and water resources at small scales. Whether precipitation products can meet the runoff simulation of small watersheds is the main purpose of this study. With NOAA-CPC-US precipitation as a reference, in nine small watersheds of the United States, accuracy of the precipitation products such as PERSIANN, PERSIANN-CDR, TRMM-3B42V7, GPM-IMERG, StageIV, and ERA5 is analyzed. By driving the CREST hydrological model using these datasets, the runoff simulation effects were evaluated. Result shows the precipitation products match the NOAA-CPC-US from high to low in the order of StageIV, PERSIANN-CDR, GPM-IMERG, PERSIANN, ERA5, and finally TRMM-3B42 V7. These datasets have relatively low accuracy in the northern high latitude area and the western mountains, while accuracy is better in the central, southern, and eastern parts of the United States. In the runoff simulation effectiveness evaluation, the daily runoff of watersheds is simulated during the same verification period. The results show that: NOAA-CPC-US and StageIV have good simulation effect in small watersheds. In the northern and western United States, the PERSIANN, PERSIANN-CDR, GPM-IMERG, and ERA5 for runoff simulation should be used with caution. TRMM-3B42V7 is not suitable for runoff simulation in small watershed. [ABSTRACT FROM AUTHOR]

The Mississippi River basin (MRB) is a flash flood hotspot receiving the most frequent flash floods and highest average rainfall accumulation of any region in the United States. Given the destruction flash floods cause in the current climate in the MRB, it is critical to understand how they will change in a future, warmer climate in order to prepare for these impacts. Recent work utilizing convection-permitting climate simulations to analyze future precipitation changes in flash flood--producing storms in the United States shows that the MRB experiences the greatest future increase in flash flood rainfall. This result motivates the goal of the present study to better understand the changes to precipitation characteristics and vertical velocity in flash flood--producing storms in the MRB. Specifically, the variations in flash flood--producing storm characteristics related to changes in vertical velocity in the MRB are examined by identifying 484 historical flash flood--producing storms from 2002 and 2013 and studying how they change in a future climate using 4-km convection-permitting simulations under a pseudo--global warming framework. In a future climate, precipitation and runoff increase by 17% and 32%, respectively, in flash flood--producing storms in the MRB. While rainfall increases in all flash flood--producing storms due to similar increases in moisture, it increases the most in storms with the strongest vertical velocity, suggesting that storm dynamics might modulate future changes in rainfall. These results are necessary to predict and prepare for the multifaceted impacts of climate change on flash flood--producing storms in order to create more resilient communities. [ABSTRACT FROM AUTHOR]

This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time. Plain Language Summary: We develop a method to estimate past, present, and future flood risk for all properties in the conterminous United States whether affected by river, coastal or rainfall flooding. The analysis accounts for variability within environmental factors including changes in sea level rise, hurricane intensity and landfall locations, precipitation patterns, and river discharge. We show that even for a conservative climate change trajectory we can expect locally significant changes in the land area at risk from floods by 2050, and by this time defenses protecting 2,200 km2 of land may be compromised. The complete dataset has been made available via a website (https://floodfactor.com/) created by the First Street Foundation in order to increase public awareness of the threat posed by flooding to safety and livelihoods. Key Points: First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal)In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D modelsBy 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West [ABSTRACT FROM AUTHOR]

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform. [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]

Weather radars and gauge observations are the primary observations to determine the coverage and magnitude of precipitation; however, radar and gauge networks have significant coverage gaps, which can underrepresent or even miss the occurrence of precipitation. This is especially noticeable in mountainous regions and in shallow precipitation regimes. The following study presents a methodology to improve spatial representations of precipitation by seamlessly blending multiple precipitation sources within the Multi-Radar Multi-Sensor (MRMS) system. A high spatiotemporal resolution multisensor merged quantitative precipitation estimation (QPE) product (MSQPE) is generated by using gauge-corrected radar QPE as a primary precipitation source with a combination of hourly gauge observations, monthly precipitation climatologies, numerical weather prediction short-term precipitation forecasts, and satellite observations to use in areas of insufficient radar coverage. The merging of the precipitation sources is dependent upon radar coverage based on an updated MRMS radar quality index, surface and atmospheric conditions, topography, gauge locations, and precipitation values. Evaluations of the MSQPE product over the western United States resulted in improved statistical measures over its individual input precipitation sources, particularly the locally gauge-corrected radar QPE. TheMSQPE scheme demonstrated its ability to sufficiently fill in areas where radar alone failed to detect precipitation due to significant beam blockage or poor coverage while minimizing the generation of false precipitation and underestimation biases that resulted from radar overshooting precipitation. [ABSTRACT FROM AUTHOR]

Deficiencies in forecast models commonly stem from inadequate representation of physical processes; yet, improvement to any single physics component within a model may lead to degradations in other physics components or the model as a whole. In this study, a systematic investigation of physics tendencies is demonstrated to help identify and correct compensating sources of model biases. The model improvement process is illustrated by addressing a commonly known issue in warm-season rainfall forecasts from parameterized convection models: the misrepresentation of the diurnal precipitation cycle over land, especially in its timing. Recent advances in closure assumptions in mass-flux cumulus schemes have made remarkable improvements in this respect. Here, we investigate these improvements in the representation of the diurnal precipitation cycle for a spring period over the United States, and how changes to the cumulus scheme impact the model climate and the behavior of other physics schemes. The modified cumulus scheme improves both the timing of the diurnal precipitation cycle and reduces midtropospheric temperature and moisture biases. However, larger temperature and moisture biases are found in the boundary layer as compared to a predecessor scheme, along with an overamplification of the diurnal precipitation cycle, relative to observations. Guided by a tendency analysis, we find that biases in the diurnal amplitude of the precipitation cycle in our simulations, along with temperature and moisture biases in the boundary layer, originate from the land surface model. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An accurate representation of MCSs' rainfall is therefore crucial in understanding their impact on the climate system. The V06B Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) half-hourly precipitation final product is a useful tool to study the precipitation characteristics of MCSs because of its global coverage and fine spatiotemporal resolutions. However, errors and uncertainties in IMERG should be quantified before applying it to hydrology and climate applications. This study evaluates IMERG performance on capturing and detecting MCSs' precipitation in the central and eastern United States during a 3-yr study period against the radar-based Stage IV product. The tracked MCSs are divided into four seasons and are analyzed separately for both datasets. IMERG shows a wet bias in total precipitation but a dry bias in hourly mean precipitation during all seasons due to the false classification of nonprecipitating pixels as precipitating. These false alarm events are possibly caused by evaporation under the cloud base or the misrepresentation of MCS cold anvil regions as precipitating clouds by the algorithm. IMERG agrees reasonably well with Stage IV in terms of the seasonal spatial distribution and diurnal cycle of MCSs precipitation. A relative humidity (RH)- based correction has been applied to the IMERG precipitation product, which helps reduce the number of false alarm pixels and improves the overall performance of IMERG with respect to Stage IV. [ABSTRACT FROM AUTHOR]

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s-1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur. [ABSTRACT FROM AUTHOR]