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Brian R. Nelson, Olivier P. Prat, D.-J. Seo, and Emad Habib

Abstract

The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are used in many studies for intercomparisons including those for satellite QPEs. An overview of the National Weather Service precipitation processing system is provided here so as to set the stage IV product in context and to provide users with some knowledge as to how it is developed. Then, an assessment of the stage IV product over the period 2002–12 is provided. The assessment shows that the stage IV product can be useful for conditional comparisons of moderate-to-heavy rainfall for select seasons and locations. When evaluating the product at the daily scale, there are many discontinuities due to the operational processing at the radar site as well as discontinuities due to the merging of data from different River Forecast Centers (RFCs) that use much different processing algorithms for generating their precipitation estimates. An assessment of the daily precipitation estimates is provided based on the cumulative distribution function for all of the daily estimates for each RFC by season. In addition it is found that the hourly estimates at certain RFCs suffer from lack of manual quality control and caution should be used.

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Hugh Morrison, Marcus van Lier-Walqui, Matthew R. Kumjian, and Olivier P. Prat

Abstract

A new framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is shown that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

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Olivier P. Prat, Brian R. Nelson, Elsa Nickl, and Ronald D. Leeper

Abstract

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

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Matthew R. Kumjian, Charlotte P. Martinkus, Olivier P. Prat, Scott Collis, Marcus van Lier-Walqui, and Hugh C. Morrison

Abstract

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of “truth” and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.

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Marcus van Lier-Walqui, Hugh Morrison, Matthew R. Kumjian, Karly J. Reimel, Olivier P. Prat, Spencer Lunderman, and Matthias Morzfeld

Abstract

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates.

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Hamed Ashouri, Kuo-Lin Hsu, Soroosh Sorooshian, Dan K. Braithwaite, Kenneth R. Knapp, L. Dewayne Cecil, Brian R. Nelson, and Olivier P. Prat

Abstract

A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.

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