Multisensor Precipitation Estimator Research Articles

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States

Abstract The Multi-Radar Multi-Sensor (MRMS) system produces a suite of hydrometeorological products that are widely used for applications such as flash flood warning operations, water resource management, and climatological studies. The MRMS radar-based quantitative precipitation estimation (QPE) products have greater challenges in the western United States compared to the eastern two-thirds of the CONUS due to terrain-related blockages and gaps in radar coverage. Further, orographic enhancement of precipitation often occurs, which is highly variable in space and time and difficult to accurately capture with physically based approaches. A deep learning approach was applied in this study to understand the correlations between several interacting variables and to obtain a more accurate precipitation estimation in these scenarios. The model presented here is a convolutional neural network (CNN), which uses spatial information from small grids of several radar variables to predict an estimated precipitation value at the central grid point. Several case analyses are presented along with a yearlong statistical evaluation. The CNN model 24-h QPE shows higher accuracy than the MRMS radar QPE for several cool season atmospheric river events. Areas of consistent improvement from the CNN model are highlighted in the discussion along with areas where the model can be further improved. The initial findings from this work help set the foundation for further exploration of machine learning techniques and products for precipitation estimation as part of the MRMS operational system. Significance Statement This study explores the development and use of a deep learning model to generate precipitation fields in the complex terrain of the western United States. Generally, the model is able to improve on the statistical performance of existing radar-based precipitation estimation methods for several case studies and over a long-term period in 2021. We explore the patterns associated with certain areas of strong performance and suggest potential means of improving areas with weaker performance. These initial results indicate the potential of deep learning to supplement radar-based approaches in areas with observational limitations.

Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities

High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation (MTG) coverage.

A Synoptic Framework for Forecasting the Urban Rainfall Effect Using Composite and K-Means Cluster Analyses

Observational and numerical modeling studies continue to affirm the existence of the “urban rainfall effect” (URE), or a discernible anomaly in warm season precipitation due to urbanization. However, the literature has been lacking a progression towards the predictability of the URE. Atlanta, Georgia has consistently appeared in the literature because of its well-studied urban rainfall anomalies. Using the Multi-sensor Precipitation Estimates (MPE) dataset and the ERA-Interim reanalysis dataset, an 18-year period (2002–2019) is examined. Three similar but distinct methods are used to define urban rainfall days (URDs), or periods when the precipitation in the urbanized areas of Atlanta are greater than the surrounding rural areas. A combination of compositing, wind rose, and k-means cluster analyses are employed to extract the synoptic framework supportive of the URE in Atlanta, Georgia. The synoptic-scale compositing analysis reveals that there are a consistent set of meteorological ingredients that are needed to produce an URD, including weaker-than-average southwesterly-to-northwesterly flow at 700 hPa, copious amounts of moisture throughout the tropospheric column, and a background low-level convergent flow. Composite atmospheric soundings reveal that there is enhanced moisture throughout the tropospheric column on URDs, leading to marginal instability that favors localized convection across the Atlanta metropolitan area. The study also provides clarity on how often the URE is present (roughly 8% of the time) during warm season days across the Atlanta metropolitan area. Taken together, this synoptic framework will aid in the forecasting of the URE in Atlanta and can be easily applied to other cities.

Evaluation of gridded precipitation datasets over international basins and large lakes

Reliable precipitation estimates are a crucial component for hydrologic modeling and hydro-climate applications. However, watersheds that extend across international boundaries or those that contain large bodies of water pose particular challenges to the acquisition of consistent and accurate precipitation estimates. The North American Great Lakes basin is characterized by both of these features, which has led to long-standing challenges to water budget analysis and hydrologic prediction. In order to provide optimal conditions for hydrologic model calibration, retrospective analyses, and real-time forecasting, this study comprehensively evaluates four gridded datasets over the Great Lakes basin, including the Analysis of Record for Calibration (AORC), Canadian Precipitation Analysis (CaPA), Multi-sensor Precipitation Estimate (MPE), and a merged CaPA-MPE. These products are analyzed at multiple spatial (overland, overlake, sub-basin, country) and temporal (daily, monthly, annual) scales using station observations and a statistical water balance model. In comparison with gauge observations from the Global Historical Climatology Network Daily (GHCN-D), gridded datasets generally agree with ground observations, however the international border clearly delineates a decrease in gridded precipitation accuracy over the Canadian portion of the basin. Analysis reveals that rank in gridded precipitation accuracy differs for overland and overlake regions, and between colder and warmer months. Overall, the AORC has the lowest variance compared to gauge observations and has greater performance over temporal and spatial scales. While CaPA and AORC may better capture atmospheric dynamics between land and lake regions, comparison with a statistical water balance model suggests that AORC and MPE provide the best estimates of monthly overlake precipitation.

Performance of Multi-Radar Multi-Sensor (MRMS) product in monitoring precipitation under extreme events in Harris County, Texas

With an increase in intensity and frequency of extreme precipitations as a result of climate change, it is necessary to develop effective strategies for emergency flood management plan. It is critical to integrate the use of high-resolution hydro-meteorological data (e.g., precipitation) as input for hydrologic modeling to accurately predict and reduce the negative impact of floods. The Multi-Radar Multi-Sensor (MRMS) system has been developed by the National Severe Storms Laboratory (NSSL)to produce high-resolution spatio-temporal precipitation data. While the MRMS data are available at relatively high spatial (1 km) and temporal (2 min) resolutions across the continental United States (CONUS), MRMS’s accuracy in measuring actual precipitation needs to be investigated across some urban areas such as Harris County, TX. Therefore, the objectives of this study are to evaluate i) the performance of the MRMS system compared to other precipitation products (rain gauge network, Multisensor Precipitation Estimator (MPE)) at different spatial (5, 10, 15, 30 km) and temporal aggregations (5, 10,15, 30, 60 min) during four major flooding events of May 2015 (Memorial Day flood), April 2016 (Tax Day Flood), August 2017 (Hurricane Harvey), and September 2019 (Tropical Storm Imelda) in Harris County, Texas; and ii) the effects of temporal and spatial aggregation scales on the performance of the MRMS system using a suite of statistical parameters. Point-to-grid comparisons were conducted between 142 rain gauges and MRMS system data during four extreme flood events. Overall, the MRMS system captured precipitation reasonably well with a coefficient of determination (R2) of 0.78, correlation coefficient (CC) of 0.88, root mean square error (RMSE) of 1.21 mm, critical success index (CSI) of 0.65, probability of detection (POD) of 0.98, and false alarm ratio (FAR) of 0.34 over Harris County at 15 min and 15 km temporal and spatial resolutions. The results indicate that MRMS product tends to underestimate higher precipitation rates and overestimate light precipitation. Coarser temporal resolutions from 5 min to 1 h resolved some of the overestimation issues. Temporal aggregation increased R2, CC, CSI, and error variances and decreased FAR. However, increasing spatial resolution from 1 to 30 km increased R2, CC, and CSI and reduced RMSE and FAR. A comparison of MPE QPE and MRMS products at hourly temporal resolution with gauge observations showed that both products estimate rainfall accurately for the four events. Still, on average, MRMS product has a slightly better agreement with rain gauge observations at 1-hr temporal resolution.

Examining the Robustness of a Spatial Bootstrap Regional Approach for Radar-Based Hourly Precipitation Frequency Analysis

Radar-based Quantitative Precipitation Estimates (QPE) provide rainfall products with high temporal and spatial resolutions as opposed to sparse observations from rain gauges. Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches.

Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

The Influence of Synoptic-Scale Air Mass Conditions on Seasonal Precipitation Patterns over North Carolina

This paper characterizes the influence of synoptic-scale air mass conditions on the spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003–2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the Coastal Plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions, whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, the highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003–2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina.

Possible hydrologic forecasting improvements resulting from advancements in precipitation estimation and forecasting for a real-time flood forecast system in the Ohio River Valley, USA

Errors in hydrometeorological forcings for hydrologic modeling lead to considerable prediction uncertainty of hydrologic variables. Analyses of Quantitative Precipitation Estimate (QPE) and Quantitative Precipitation Forecast (QPF) errors over the Ohio River Valley were made to quantify QPE and QPF errors and identify hydrologic impacts of forcing errors and possible improvements resulting from advancements in precipitation estimation and forecasting. Monthly, seasonal, and annual bias analyses of Ohio River Forecast Center (OHRFC) NEXt-generation RADar (NEXRAD) based Stage III and Multisensor Precipitation Estimator (MPE) precipitation estimates, for the period 1997-2016, were made with respect to Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation estimates. Verification of QPF from NWS River Forecast Centers from the NOAA/NWS National Precipitation Verification Unit (NPVU) was compared to QPF verification measures from several numerical weather prediction models and the NOAA/NWS Weather Prediction Center (WPC). Improvements in NEXRAD based QPE over the OHRFC area have been dramatic from 1997 to present. However, from the perspective of meeting hydrologic forecasting needs, QPF shows marginal improvement. A hydrologic simulation experiment illustrates the sensitivity of hydrologic forecasts to QPF errors indicated by QPF Threat Score (TS) values. A monte carlo experiment shows there can be considerable hydrologic forecast error associated with QPF at expected WPC TS levels and, importantly, that higher TS values do not necessarily translate into improved hydrologic forecasts. These results have significant implications for real-time hydrologic forecasting. First, experimental results demonstrate the value gained in terms of improvements in hydrologic modeling accuracy from long-term radar-based precipitation bias reductions. Second, experimental results show that improvements made in terms of QPF have marginal effect on hydrologic prediction and that, even with significant improvement in QPF accuracy, we should expect large hydrologic prediction uncertainty. These issues are discussed more fully as they relate to the future of operational hydrology and ensemble hydrologic forecasting.

Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging

AbstractSoil moisture spatial patterns with length scales of 1‐100 km influence hydrological, ecological, and agricultural processes, but the footprint or support volume of existing monitoring systems, for example, satellite‐based radiometers and sparse in situ monitoring networks, is often either too large or too small to effectively observe these mesoscale patterns. This measurement scale gap hinders our understanding of soil water processes and complicates calibration and validation of hydrologic models and soil moisture satellites. One possible solution is to utilize geostatistical techniques that have proven effective for mapping static patterns in soil properties. The objective of this study was to determine how effectively dynamic, mesoscale soil moisture patterns can be mapped by applying regression kriging to the data from a sparse, large‐scale in situ network. The fully automated system developed here uses several data sets: daily soil moisture measurements from the Oklahoma Mesonet, sand content estimates from the Natural Resource Conservation Service Soil Survey Geographic Database, and an antecedent precipitation index computed from National Weather Service multisensor precipitation estimates. A multiple linear regression model is fitted daily to the observed data, and the residuals of that model are used in a semivariogram estimation and kriging routine to produce daily statewide maps of soil moisture at 5‐, 25‐, and 60‐cm depths at 800‐m resolution. During over 3 years of operation, this mapping system has revealed complex, dynamic, and depth‐specific mesoscale patterns, reflecting the shifting influences of both soil texture and precipitation, with a mean absolute error of ≤0.0576 cm3/cm3 across all three depths.

Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service) operations) over two regions: (I) Inside radar effective coverage and (II) immediately outside radar coverage. The outcomes of the evaluation indicate (a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Observing the May 2015 Record Rainfall at Norman, Oklahoma, Using Various Methods

Abstract The May 2015 record rainfall that occurred across Oklahoma was the result of a large number of high-intensity rain events. A unique set of observations from gauges in the Oklahoma Mesonet, the NWS Cooperative Observer (COOP) network, the Community Collaborative Rain, Hail and Snow (CoCoRaHS) network, an experimental pit gauge system, and NWS radar was available that covered an area in and around Norman, Oklahoma. This paper documents the performance of the various gauges throughout the course of the month. Key findings are 1) observations from all methods significantly exceeded the 200-yr return interval; 2) a weighing-bucket gauge at ground level recorded amounts up to 4.5% higher than a similarly located ground-level tipping-bucket gauge and up to 8.2% higher than a nearby aboveground tipping-bucket gauge; 3) a manual COOP gauge recorded nearly identical (within 1.2%) observations as compared to an automated tipping-bucket gauge at a collocated Mesonet station; and 4) observations from 26 CoCoRaHS gauges yielded an average rainfall within 1% of the aerially averaged radar rainfall derived from the Multisensor Precipitation Estimator.

Spatio-temporal rainfall patterns around Atlanta, Georgia and possible relationships to urban land cover

Spatio-temporal patterns in mean and extreme rainfall are examined around the city of Atlanta, Georgia using the Multi-sensor Precipitation Estimates (MPE) and ERA-Interim reanalysis datasets. The analysis spans the period 2002 to 2015 and employs a 9-cell gridded framework centered on downtown Atlanta. Statistically significant anomalies in daily precipitation were found over and downwind (predominately east to northeast) of Atlanta. The pattern of rainfall anomalies is most evident in the early evening hours of the day and is hypothesized to be related to the evolution of the skin or surface urban heat island (UHI), rather than the canopy layer UHI. The study formally proposes the term “flow regime dependent” downwind anomaly regions. Like previous results, the study reveals that downwind anomaly regions can vary as a function of prevailing wind regime. Using a metric called the Wet Millimeter Day (WMD), the study also finds that there is a tendency for extreme rainfall to cluster in the climatological downwind area of Atlanta. The work builds upon previous findings while employing different datasets to provide novel additional contributions related to the temporal evolution of the “urban rainfall effect” and the patterns of extreme rainfall.

Evaluating the MSG satellite Multi-Sensor Precipitation Estimate for extreme rainfall monitoring over northern Tunisia

Abstract Knowledge and evaluation of extreme precipitation is important for water resources and flood risk management, soil and land degradation, and other environmental issues. Due to the high potential threat to local infrastructure, such as buildings, roads and power supplies, heavy precipitation can have an important social and economic impact on society. At present, satellite derived precipitation estimates are becoming more readily available. This paper aims to investigate the potential use of the Meteosat Second Generation (MSG) Multi-Sensor Precipitation Estimate (MPE) for extreme rainfall assessment in Tunisia. The MSGMPE data combine microwave rain rate estimations with SEVIRI thermal infrared channel data, using an EUMETSAT production chain in near real time mode. The MPE data can therefore be used in a now-casting mode, and are potentially useful for extreme weather early warning and monitoring. Daily precipitation observed across an in situ gauge network in the north of Tunisia were used during the period 2007–2009 for validation of the MPE extreme event data. As a first test of the MSGMPE product's performance, very light to moderate rainfall classes, occurring between January and October 2007, were evaluated. Extreme rainfall events were then selected, using a threshold criterion for large rainfall depth (>50 mm/day) occurring at least at one ground station. Spatial interpolation methods were applied to generate rainfall maps for the drier summer season (from May to October) and the wet winter season (from November to April). Interpolated gauge rainfall maps were then compared to MSGMPE data available from the EUMETSAT UMARF archive or from the GEONETCast direct dissemination system. The summation of the MPE data at 5 and/or 15 min time intervals over a 24 h period, provided a basis for comparison. The MSGMPE product was not very effective in the detection of very light and light rain events. Better results were obtained for the slightly more moderate and moderate rain event classes in terms of percentage of detected events, correlation coefficient, and ratio bias. The results for extreme events were mixed, with high pixel correlations of R=0.75 achieved for some events, while for other events the correlation between satellite and ground observation was rather weak. MPE data for northern Tunisia seem more reliable during the summer season and for larger event scales. The MSGMPE data have demonstrated to be very informative for early warning purposes, but need to be combined with other near real time data or information to give reliable and quantitative estimates of extreme rainfall.

The Likelihood of Coliform Bacteria in NJ Domestic Wells Based on Precipitation and Other Factors.

The influence of precipitation on coliform bacteria detection rates in domestic wells was investigated using data collected through the New Jersey Private Well Testing Act. Measured precipitation data from the National Weather Service (NWS) monitoring stations was compared to estimated data from the Multisensor Precipitation Estimate (MPE) in order to determine which source of data to include in the analyses. A strong concordance existed between these two precipitations datasets; therefore, MPE data was utilized as it is geographically more specific to individual wells. Statewide, 10 days of cumulative precipitation prior to testing was found to be an optimal period influencing the likelihood of coliform detections in wells. A logistic regression model was developed to predict the likelihood of coliform occurrence in wells from 10 days of cumulative precipitation data and other predictive variables including geology, season, coliform bacteria analysis method, pH, and nitrate concentration. Total coliform (TC) and fecal coliform or Escherichia coli (FC/EC) were detected more frequently when the preceding 10 days of cumulative precipitation exceeded 34.5 and 54 mm, respectively. Furthermore, the likelihood of coliform detection was highest in wells located in the bedrock region, during summer and autumn, analyzed with the enzyme substrate method, with pH between 5 and 6.99, and (for FC/EC but not TC) nitrate greater than 10 mg/L. Thus, the likelihood of coliform presence in domestic wells can be predicted from readily available environmental factors including timing and magnitude of precipitation, offering outreach opportunities and potential changes to coliform testing recommendations.

Recursive estimators of mean-areal and local bias in precipitation products that account for conditional bias

This paper presents novel formulations of Mean field bias (MFB) and local bias (LB) correction schemes that incorporate conditional bias (CB) penalty. These schemes are based on the operational MFB and LB algorithms in the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE). By incorporating CB penalty in the cost function of exponential smoothers, we are able to derive augmented versions of recursive estimators of MFB and LB. Two extended versions of MFB algorithms are presented, one incorporating spatial variation of gauge locations only (MFB-L), and the second integrating both gauge locations and CB penalty (MFB-X). These two MFB schemes and the extended LB scheme (LB-X) are assessed relative to the original MFB and LB algorithms (referred to as MFB-O and LB-O, respectively) through a retrospective experiment over a radar domain in north-central Texas, and through a synthetic experiment over the Mid-Atlantic region. The outcome of the former experiment indicates that introducing the CB penalty to the MFB formulation leads to small, but consistent improvements in bias and CB, while its impacts on hourly correlation and Root Mean Square Error (RMSE) are mixed. Incorporating CB penalty in LB formulation tends to improve the RMSE at high rainfall thresholds, but its impacts on bias are also mixed. The synthetic experiment suggests that beneficial impacts are more conspicuous at low gauge density (9 per 58,000 km2), and tend to diminish at higher gauge density. The improvement at high rainfall intensity is partly an outcome of the conservativeness of the extended LB scheme. This conservativeness arises in part from the more frequent presence of negative eigenvalues in the extended covariance matrix which leads to no, or smaller incremental changes to the smoothed rainfall amounts.

Eastern U.S. Verification of Ensemble Precipitation Forecasts

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.

A statistical, spatial, and hydrologic comparison of gauge-based and MPE-based rainfall measurements

Gauge-based and multi-sensor precipitation estimation (MPE) data are compared on hourly, daily, monthly and event time scales at site locations over a 12-year period. Gauge data is collected at 16 sites within a 950 km2 portion of the Upper Iowa River in northeast Iowa. Average relative MPE bias is positive for all but the event time scale, and has a magnitude of less than 0.10 for all scales. Gauge and MPE average correlation coefficients range from 0.73 on the hourly scale to 0.92 on the event and monthly scales. The MPE relative bias standard deviation decreases from 1.70 mm on the hourly scale to 0.27 mm on the monthly scale. Decomposition of hourly bias reveals that the false positive portion is the most significant component. Seventy percent of MPE accumulation have a relative bias of 0.5 or less when hourly accumulations are 7 mm or greater. Pearson product-moment coefficient analysis reveals strong similarities in spatial correlations as a function of site separation. Rainfall time series for the basin are constructed from the two data sources and used as input to a Blocked Topmodel rainfall runoff scheme to provide another means of comparison on a basin-wide spatial scale. Five goodness-of-fit measures are used for quantifying the viability of simulated flows. No statistically significant difference in annual means using the difference sources is found for any of the measures.

Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation

A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC>0.9 and hourly gauge precipitation is >60mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12mm while the reduction in RMSE by OCK over RO is about 7mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance.