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James D. Brown and Dong-Jun Seo

Abstract

This paper describes a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast. This ccdf represents the “true” probability distribution of the forecast variable, subject to sampling uncertainties. In the absence of a known distributional form, the ccdf should be estimated nonparametrically. It is noted that the probability of exceeding a threshold of the observed variable, such as flood stage, is equivalent to the expectation of an indicator variable defined for that threshold. The ccdf is then modeled through a linear combination of the indicator variables of the forecast ensemble members. The technique is based on Bayesian optimal linear estimation of indicator variables and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator variable at each threshold, it effectively minimizes the continuous ranked probability score (CRPS) when infinitely many thresholds are employed. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS) and streamflow ensemble forecasts from the National Weather Service (NWS) River Forecast Centers (RFCs). Split-sample validation results are presented for several attributes of ensemble forecast quality, including reliability and discrimination. In general, the forecast biases were substantially reduced following ICK. Overall, the technique shows significant potential for bias-correcting ensemble forecasts whose distributional form is unknown or nonparametric.

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Dongsoo Kim, Brian Nelson, and Dong-Jun Seo

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The Hydrometeorological Automated Data System (HADS) is a real-time data acquisition, processing, and distribution system operated by the Office of Hydrologic Development (OHD) of NOAA’s National Weather Service (NWS). The initial reprocessing of HADS data from its original format since its inception in July 1996 has been completed at NOAA’s National Climatic Data Center (NCDC). The quality of the reprocessed HADS hourly precipitation data from rain gauges is assessed by two objective metrics: the average fraction of missing values and the percentage of top-of-the-hour observations for a 3-yr period (2003–05). Pairwise comparisons between the reprocessed product and the real-time product are made using representative samples (about 13%) from the 48 contiguous United States. The monthly average of missing values varies from 0.5% to 2% in the reprocessed product and from 1.7% to 10.1% in the real-time product. Except for January 2003, the reprocessed product consistently reduced missing values, by as much as 9.4% in October 2004. The availability of top-of-the-hour observations is about 85% in the reprocessed product, while the real-time product has top-of-the-hour observations only about 50% of the time. This paper discusses real-time product quality issues, additional quality assurance algorithms used in the reprocessing environment, and the design of system-wide performance comparisons. Thus, the benefits to users of reprocessing the HADS data are the correction of 4-h observation time errors during 1 July–11 August 2005 and the demonstration of diurnals pattern of precipitation frequencies in regional domains. A Web-based interactive quality assessment tool for reprocessed HADS hourly precipitation data and access to the data are also presented.

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Mohammadvaghef Ghazvinian, Yu Zhang, and Dong-Jun Seo

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This paper introduces a new, two-part scheme for postprocessing single-valued precipitation forecast to create probabilistic quantitative precipitation forecast (PQPF). This scheme, herein referred to as the mixed-type nonhomogeneous regression (MNHR), combines the use of logistic regression for estimating rainfall intermittency and nonhomogeneous regression for estimation of additional parameters of the conditional distribution. The performance of MNHR is evaluated relative to operational mixed-type meta-Gaussian distribution (MMGD) and the censored, shifted gamma distribution (CSGD) in postprocessing Global Ensemble Forecast System (GEFS) reforecasts averaged over 25 watersheds in the American River basin in California. The results point to superior performance of MNHR relative to MMGD and CSGD in terms of the skill of postprocessed PQPFs at 24- and 96-h accumulation windows. In addition, it is observed that the performance of CSGD tends to trail behind MNHR and MMGD at least for the 24-h window, though the performance differences tend to narrow at higher forecast amounts and longer lead times. Our analyses suggest that CSGD’s underperformance arises partly from its tendency to inflate the shift parameter estimates, which is pronounced over the study site possibly because of infrequent rainfall occurrence. By contrast, MNHR’s use of logistic regression helps avoid such bias, and its formulation of conditional distribution addresses the lack of skewness of MMGD for higher forecast amounts. Moreover, MHNR-based PQPF exhibits both superior calibration and relatively high sharpness at short lead times and on an unconditional sense, whereas it features lower sharpness relative to the other two suites when conditioned on higher forecast amount. This trade-off between calibration and conditional sharpness warrants further research.

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Dong-Jun Seo, Victor Koren, and Neftali Cajina

Abstract

Variational assimilation (VAR) of hydrologic and hydrometeorological data into operational hydrologic forecasting is explored. The data assimilated are the hourly real-time observations of streamflow and precipitation, and climatological estimates of potential evaporation (PE). The hydrologic system considered is a single headwater basin for which soil moisture accounting and routing are carried out in a lumped fashion via the Sacramento model (SAC) and the unit hydrograph (UH), respectively. The control variables in the VAR formulation are the fast-varying SAC soil moisture states at the beginning of the assimilation window and the multiplicative adjustment factors to the estimates of mean areal precipitation (MAP) and mean areal potential evaporation (MAPE) for each hour in the assimilation window. In a separate application of VAR as a parameter estimation tool, the estimation of empirical UH is also explored by treating its ordinates as the control variables. To evaluate the assimilation procedure thus developed, streamflow was forecast with and without the aid of VAR for three basins in the southern plains under the assumption of perfectly forecast future mean areal precipitation (FMAP). The streamflow forecasts were then compared with each other and with those based on persistence and the state space-based state-updating procedure, the state-space Sacramento model (SS-SAC). The results indicate that the VAR procedure significantly improves the accuracy of the basic forecast at short lead times and compares favorably with SS-SAC.

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Dong-Jun Seo and J. P. Breidenbach

Abstract

A procedure for real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements is described. Developed to complement the existing gauge-based bias correction procedures used in the National Weather Service (NWS), the proposed procedure is a generalized local bias estimator that may be used under varying conditions of rain gauge network density and types of rainfall. To arrive at the procedure, the correction problem is formulated as a space–time estimation of radar and bin-averaged gauge rainfall from radar rainfall data and rain gauge measurements, respectively, at all hours up to and including the current hour. The estimation problem is then solved suboptimally via a variant of exponential smoothing. To evaluate the procedure, parameter estimation and true validation were performed using hourly radar-rainfall and rain gauge data from the Arkansas–Red Basin River Forecast Center (ABRFC) area. The results indicate that the proposed procedure is generally superior to mean field bias correction, and that the improvement is particularly significant in the cool season.

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James D. Brown, Dong-Jun Seo, and Jun Du

Abstract

Precipitation forecasts from the Short-Range Ensemble Forecast (SREF) system of the National Centers for Environmental Prediction (NCEP) are verified for the period April 2006–August 2010. Verification is conducted for 10–20 hydrologic basins in each of the following: the middle Atlantic, the southern plains, the windward slopes of the Sierra Nevada, and the foothills of the Cascade Range in the Pacific Northwest. Mean areal precipitation is verified conditionally upon forecast lead time, amount of precipitation, season, forecast valid time, and accumulation period. The stationary block bootstrap is used to quantify the sampling uncertainties of the verification metrics. In general, the forecasts are more skillful for moderate precipitation amounts than either light or heavy precipitation. This originates from a threshold-dependent conditional bias in the ensemble mean forecast. Specifically, the forecasts overestimate low observed precipitation and underestimate high precipitation (a type-II conditional bias). Also, the forecast probabilities are generally overconfident (a type-I conditional bias), except for basins in the southern plains, where forecasts of moderate to high precipitation are reliable. Depending on location, different types of bias correction may be needed. Overall, the northwest basins show the greatest potential for statistical postprocessing, particularly during the cool season, when the type-I conditional bias and correlations are both high. The basins of the middle Atlantic and southern plains show less potential for statistical postprocessing, as the type-II conditional bias is larger and the correlations are weaker. In the Sierra Nevada, the greatest benefits of statistical postprocessing should be expected for light precipitation, specifically during the warm season, when the type-I conditional bias is large and the correlations are strong.

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Haksu Lee, Yu Zhang, Dong-Jun Seo, Robert J. Kuligowski, David Kitzmiller, and Robert Corby

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This study examines the utility of satellite-based quantitative precipitation estimates (QPEs) from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for hydrologic prediction. In this work, two sets of SCaMPR QPEs, one without and the other with Tropical Rainfall Measurement Mission (TRMM) version 6 data integrated, were used as input forcing to the lumped National Weather Service hydrologic model to retrospectively generate flow simulations for 10 Texas catchments over 2000–07. The year 2000 was used for the model spinup, 2001–04 for calibration, and 2005–07 for validation. The results were validated using observed streamflow alongside similar simulations obtained using interpolated gauge QPEs with varying gauge network densities, and still others using the operational radar–gauge multisensor product (MAPX). The focus of the evaluation was on the high-flow events. A number of factors that could impact the relative utility of SCaMPR satellite QPE and gauge-only analysis (GMOSAIC) for flood prediction were examined, namely, 1) the incremental impacts of TRMM version 6 data ingest, 2) gauge density, 3) effects of calibration approaches, and 4) basin properties. Results indicate that ground-sensor-based QPEs in a broad sense outperform SCaMPR QPEs, while SCaMPR QPEs are competitive in a minority of catchments. TRMM ingest helped substantially improve the SCaMPR QPE–based simulation results. Change in calibration forcing, that is, calibrating the model using individual QPEs rather than the MAPX (the most accurate QPE), yielded overall improvements to the simulation accuracy but did not change the relative performance of the QPEs.

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Richard A. Fulton, Jay P. Breidenbach, Dong-Jun Seo, Dennis A. Miller, and Timothy O’Bannon

Abstract

A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service’s warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.

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Babak Alizadeh, Reza Ahmad Limon, Dong-Jun Seo, Haksu Lee, and James Brown

Abstract

A novel multiscale postprocessor for ensemble streamflow prediction, MS-EnsPost, is described and comparatively evaluated with the existing postprocessor in the National Weather Service’s Hydrologic Ensemble Forecast Service, EnsPost. MS-EnsPost uses data-driven correction of magnitude-dependent bias in simulated flow, multiscale regression using observed and simulated flows over a range of temporal aggregation scales, and ensemble generation using parsimonious error modeling. For comparative evaluation, 139 basins in eight River Forecast Centers in the United States were used. Streamflow predictability in different hydroclimatological regions is assessed and characterized, and gains by MS-EnsPost over EnsPost are attributed. The ensemble mean and ensemble prediction results indicate that, compared to EnsPost, MS-EnsPost reduces the root-mean-square error and mean continuous ranked probability score of day-1 to day-7 predictions of mean daily flow by 5%–68% and by 2%–62%, respectively. The deterministic and probabilistic results indicate that for most basins the improvement by MS-EnsPost is due to both magnitude-dependent bias correction and full utilization of hydrologic memory through multiscale regression. Comparison of the continuous ranked probability skill score results with hydroclimatic indices indicates that the skill of ensemble streamflow prediction with post processing is modulated largely by the fraction of precipitation as snowfall and, for non-snow-driven basins, mean annual precipitation.

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Kyong-Hwan Seo, Jung Ok, Jun-Hyeok Son, and Dong-Hyun Cha

Abstract

Future changes in the East Asian summer monsoon (EASM) are estimated from historical and Representative Concentration Pathway 6.0 (RCP6) experiments of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The historical runs show that, like the CMIP3 models, the CMIP5 models produce slightly smaller precipitation. A moisture budget analysis illustrates that this precipitation deficit is due to an underestimation in evaporation and ensuing moisture flux convergence. Of the two components of the moisture flux convergence (i.e., moisture convergence and horizontal moist advection), moisture convergence associated with mass convergence is underestimated to a greater degree.

Precipitation is anticipated to increase by 10%–15% toward the end of the twenty-first century over the major monsoonal front region. A statistically significant increase is predicted to occur mostly over the Baiu region and to the north and northeast of the Korean Peninsula. This increase is attributed to an increase in evaporation and moist flux convergence (with enhanced moisture convergence contributing the most) induced by the northwestward strengthening of the North Pacific subtropical high (NPSH), a characteristic feature of the future EASM that occurred in CMIP5 simulations. Along the northern and northwestern flank of the strengthened NPSH, intensified southerly or southwesterly winds lead to the increase in moist convergence, enhancing precipitation over these areas. However, future precipitation over the East China Sea is projected to decrease. In the EASM domain, a local mechanism prevails, with increased moisture and moisture convergence leading to a greater increase in moist static energy in the lower troposphere than in the upper troposphere, reducing tropospheric stability.

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