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Ralph Ferraro, Paul Pellegrino, Michael Turk, Wanchun Chen, Shuang Qiu, Robert Kuligowski, Sheldon Kusselson, Antonio Irving, Stan Kidder, and John Knaff

Abstract

Satellite analysts at the Satellite Services Division (SSD) of the National Environmental, Satellite, Data, and Information Service (NESDIS) routinely generate 24-h rainfall potential for all tropical systems that are expected to make landfall within 24 to at most 36 h and are of tropical storm or greater strength (>65 km h−1). These estimates, known as the tropical rainfall potential (TRaP), are generated in an objective manner by taking instantaneous rainfall estimates from passive microwave sensors, advecting this rainfall pattern along the predicted storm track, and accumulating rainfall over the next 24 h.

In this study, the TRaPs generated by SSD during the 2002 Atlantic hurricane season have been validated using National Centers for Environmental Prediction (NCEP) stage IV hourly rainfall estimates. An objective validation package was used to generate common statistics such as correlation, bias, root-mean-square error, etc. It was found that by changing the minimum rain-rate threshold, the results could be drastically different. It was determined that a minimum threshold of 25.4 mm day−1 was appropriate for use with TRaP. By stratifying the data by different criteria, it was discovered that the TRaPs generated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, with its optimal set of measurement frequencies, improved spatial resolution, and advanced retrieval algorithm, produced the best results. In addition, the best results were found for TRaPs generated for storms that were between 12 and 18 h from landfall. Since the TRaP is highly dependent on the forecast track of the storm, selected TRaPs were rerun using the observed track contained in the NOAA/Tropical Prediction Center (TPC) “best track.” Although some TRaPs were not significantly improved by using this best track, significant improvements were realized in some instances. Finally, as a benchmark for the usefulness of TRaP, comparisons were made to Eta Model 24-h precipitation forecasts as well as three climatological maximum rainfall methods. It was apparent that the satellite-based TRaP outperforms the Eta Model in virtually every statistical category, while the climatological methods produced maximum rainfall totals closer to the stage IV maximum amounts when compared with TRaP, although these methods are for storm totals while TRaP is for a 24-h period.

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Stanley Q. Kidder, John A. Knaff, Sheldon J. Kusselson, Michael Turk, Ralph R. Ferraro, and Robert J. Kuligowski

Abstract

Inland flooding caused by heavy rainfall from landfalling tropical cyclones is a significant threat to life and property. The tropical rainfall potential (TRaP) technique, which couples satellite estimates of rain rate in tropical cyclones with track forecasts to produce a forecast of 24-h rainfall from a storm, was developed to better estimate the magnitude of this threat. This paper outlines the history of the TRaP technique, details its current algorithms, and offers examples of its use in forecasting. Part II of this paper covers verification of the technique.

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Pingping Xie, John E. Janowiak, Phillip A. Arkin, Robert Adler, Arnold Gruber, Ralph Ferraro, George J. Huffman, and Scott Curtis

Abstract

As part of the Global Precipitation Climatology Project (GPCP), analyses of pentad precipitation have been constructed on a 2.5° latitude–longitude grid over the globe for a 23-yr period from 1979 to 2001 by adjusting the pentad Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) against the monthly GPCP-merged analyses. This adjustment is essential because the precipitation magnitude in the pentad CMAP is not consistent with that in the monthly CMAP or monthly GPCP datasets primarily due to the differences in the input data sources and merging algorithms, causing problems in applications where joint use of the pentad and monthly datasets is necessary. First, pentad CMAP-merged analyses are created by merging several kinds of individual data sources including gauge-based analyses of pentad precipitation, and estimates inferred from satellite observations. The pentad CMAP dataset is then adjusted by the monthly GPCP-merged analyses so that the adjusted pentad analyses match the monthly GPCP in magnitude while the high-frequency components in the pentad CMAP are retained. The adjusted analyses, called the GPCP-merged analyses of pentad precipitation, are compared to several gauge-based datasets. The results show that the pentad GPCP analyses reproduced spatial distribution patterns of total precipitation and temporal variations of submonthly scales with relatively high quality especially over land. Simple applications of the 23-yr dataset demonstrate that it is useful in monitoring and diagnosing intraseasonal variability. The Pentad GPCP has been accepted by the GPCP as one of its official products and is being updated on a quasi-real-time basis.

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Baijun Tian, Huikyo Lee, Duane E. Waliser, Robert Ferraro, Jinwon Kim, Jonathan Case, Takamichi Iguchi, Eric Kemp, Di Wu, William Putman, and Weile Wang

Abstract

Several dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors’ efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmöller diagram, and diurnal spatial pattern over the USGP from the simulations focusing on the summer precipitation diurnal cycle related to mesoscale convective systems (MCSs). The metric T skill scores increase significantly from the control simulation to the nudged simulations and from the nudged simulations with shorter wavelengths to the nudged simulations with longer wavelengths, but do not change much from MERRA-2 to the downscaled simulations or between the various downscaled simulations with different spatial resolutions. Thus, there is some credibility, but no significant value added compared to MERRA-2, of the downscaled climate simulations of the summer precipitation over the USGP.

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Dean N. Williams, V. Balaji, Luca Cinquini, Sébastien Denvil, Daniel Duffy, Ben Evans, Robert Ferraro, Rose Hansen, Michael Lautenschlager, and Claire Trenham

Abstract

Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.

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Robert F. Adler, George J. Huffman, Alfred Chang, Ralph Ferraro, Ping-Ping Xie, John Janowiak, Bruno Rudolf, Udo Schneider, Scott Curtis, David Bolvin, Arnold Gruber, Joel Susskind, Philip Arkin, and Eric Nelkin

Abstract

The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5° latitude × 2.5° longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.

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Takamichi Iguchi, Wei-Kuo Tao, Di Wu, Christa Peters-Lidard, Joseph A. Santanello, Eric Kemp, Yudong Tian, Jonathan Case, Weile Wang, Robert Ferraro, Duane Waliser, Jinwon Kim, Huikyo Lee, Bin Guan, Baijun Tian, and Paul Loikith

Abstract

This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation.

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