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Robert J. Joyce and Pingping Xie

Abstract

A Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH) algorithm is developed to integrate the passive microwave (PMW) precipitation estimates from low-Earth-orbit (LEO) satellites and infrared (IR) observations from geostationary (GEO) platforms. With the new algorithm, the precipitation analysis at a grid box of 8 × 8 km2 is defined in three steps. First, PMW estimates of instantaneous rain rates closest to the target analysis time in both the forward and backward directions are propagated from their observation times to the analysis time using the cloud system advection vectors (CSAVs) computed from the GEO–IR images. The “prediction” of the precipitation analysis is then defined by averaging the forward- and backward-propagated PMW estimates with weights inversely proportional to their error variance. The IR-based precipitation estimates are incorporated if the gap between the two PMW observations is longer than 90 min. Validation tests showed substantial improvements of the KF-based CMORPH against the original version in both the pattern correlation and fidelity of probability density function (PDF) of the precipitation intensity. In general, performance of the original CMORPH degrades sharply with poor pattern correlation and substantially elevated (damped) frequency for light (heavy) precipitation events when PMW precipitation estimates are available from fewer LEO satellites. The KF-based CMORPH is capable of producing high-resolution precipitation analysis with much more stable performance with various levels of availability for the PMW observations.

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John E. Janowiak, Robert J. Joyce, and Yelena Yarosh

A system has been developed and implemented that merges pixel resolution (~4 km) infrared (IR) satellite data from all available geostationary meteorological satellites into a global (60°N–60°S) product. The resulting research-quality, nearly seamless global array of information is made possible by recent work by Joyce et al., who developed a technique to correct IR temperatures at targets far from satellite nadir. At such locations, IR temperatures are colder than if identical features were measured at a target near satellite nadir. This correction procedure yields a dataset that is considerably more amenable to quantitative manipulation than if the data from the individual satellites were merely spliced together.

Several unique features of this product exist. First, the data from individual geostationary satellites have been merged to form nearly seamless maps after correcting the IR brightness temperatures for viewing angle effects. Second, with the availability of IR data from the Meteosat-5 satellite (currently positioned at a subsatellite longitude of 63°E), globally complete (60°N–60°S) fields can be produced. Third, the data have been transformed from the native satellite projection of each individual geostationary satellite and have been remapped to a uniform latitude/longitude grid. Fourth, globally merged datasets of full resolution IR brightness temperature have been produced routinely every half hour since November 1998. Fifth, seven days of globally merged, half-hourly data are available on a rotating archive that is maintained by the Climate Prediction Center Web page (http://www.cpc.ncep.noaa.gov/products/global_precip/html/web.html). Unfortunately, international agreement prevents us from distributing Meteosat data within three days of real time, so the data availability is delayed appropriately. Finally, these data are permanently saved at the National Climatic Data Center in Asheville, North Carolina, beginning with data in mid-September of 1999.

In this paper, the authors briefly describe the merging methodology and describe key aspects of the merged product. Present and potential applications of this dataset are also discussed. Applications include near-real time global disaster monitoring and mitigation and assimilation of these data into numerical weather prediction models and research, among others.

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Emad Habib, Alemseged Tamiru Haile, Yudong Tian, and Robert J. Joyce

Abstract

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.

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John E. Janowiak, Valery J. Dagostaro, Vernon E. Kousky, and Robert J. Joyce

Abstract

Summertime rainfall over the United States and Mexico is examined and is compared with forecasts from operational numerical prediction models. In particular, the distribution of rainfall amounts is examined and the diurnal cycle of rainfall is investigated and compared with the model forecasts. This study focuses on a 35-day period (12 July–15 August 2004) that occurred amid the North American Monsoon Experiment (NAME) field campaign. Three-hour precipitation forecasts from the numerical models were validated against satellite-derived estimates of rainfall that were adjusted by daily rain gauge data to remove bias from the remotely sensed estimates. The model forecasts that are evaluated are for the 36–60-h period after the model initial run time so that the effects of updated observational data are reduced substantially and a more direct evaluation of the model precipitation parameterization can be accomplished.

The main findings of this study show that the effective spatial resolution of the model-generated precipitation is considerably more coarse than the native model resolution. On a national scale, the models overforecast the frequency of rainfall events in the 1–75 mm day−1 range and underforecast heavy events (>85 mm day−1). The models also have a diurnal cycle that peaks 3–6 h earlier than is observed over portions of the eastern United States and the NAME tier-1 region. Time series and harmonic analysis are used to identify where the models perform well and poorly in characterizing the amplitude and phase of the diurnal cycle of precipitation.

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Robert J. Joyce, John E. Janowiak, Phillip A. Arkin, and Pingping Xie

Abstract

A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors derived from geostationary satellite infrared data. The Climate Prediction Center morphing method (CMORPH) uses motion vectors derived from half-hourly interval geostationary satellite IR imagery to propagate the relatively high quality precipitation estimates derived from passive microwave data. In addition, the shape and intensity of the precipitation features are modified (morphed) during the time between microwave sensor scans by performing a time-weighted linear interpolation. This process yields spatially and temporally complete microwave-derived precipitation analyses, independent of the infrared temperature field. CMORPH showed substantial improvements over both simple averaging of the microwave estimates and over techniques that blend microwave and infrared information but that derive estimates of precipitation from infrared data when passive microwave information is unavailable. In particular, CMORPH outperforms these blended techniques in terms of daily spatial correlation with a validating rain gauge analysis over Australia by an average of 0.14, 0.27, 0.26, 0.22, and 0.20 for April, May, June–August, September, and October 2003, respectively. CMORPH also yields higher equitable threat scores over Australia for the same periods by an average of 0.11, 0.14, 0.13, 0.14, and 0.13. Over the United States for June–August, September, and October 2003, spatial correlation was higher for CMORPH relative to the average of the same techniques by an average of 0.10, 0.13, and 0.13, respectively, and equitable threat scores were higher by an average of 0.06, 0.09, and 0.10, respectively.

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George J. Huffman, Robert F. Adler, Mark M. Morrissey, David T. Bolvin, Scott Curtis, Robert Joyce, Brad McGavock, and Joel Susskind

Abstract

The One-Degree Daily (1DD) technique is described for producing globally complete daily estimates of precipitation on a 1° × 1° lat/long grid from currently available observational data. Where possible (40°N–40°S), the Threshold-Matched Precipitation Index (TMPI) provides precipitation estimates in which the 3-hourly infrared brightness temperatures (IR T b) are compared with a threshold and all “cold” pixels are given a single precipitation rate. This approach is an adaptation of the Geostationary Operational Environmental Satellite Precipitation Index, but for the TMPI the IR T b threshold and conditional rain rate are set locally by month from Special Sensor Microwave Imager–based precipitation frequency and the Global Precipitation Climatology Project (GPCP) satellite–gauge (SG) combined monthly precipitation estimate, respectively. At higher latitudes the 1DD features a rescaled daily Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) precipitation. The frequency of rain days in the TOVS is scaled down to match that in the TMPI at the data boundaries, and the resulting nonzero TOVS values are scaled locally to sum to the SG (which is a globally complete monthly product).

The GPCP has approved the 1DD as an official product, and data have been produced for 1997 through 1999, with production continuing a few months behind real time (to allow access to monthly input data). The time series of the daily 1DD global images shows good continuity in time and across the data boundaries. Various examples are shown to illustrate uses. Validation for individual gridbox values shows a very high mean absolute error, but it improves quickly when users perform time/space averaging according to their own requirements.

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David J. Diner, Thomas P. Ackerman, Theodore L. Anderson, Jens Bösenberg, Amy J. Braverman, Robert J. Charlson, William D. Collins, Roger Davies, Brent N. Holben, Chris A . Hostetler, Ralph A. Kahn, John V. Martonchik, Robert T. Menzies, Mark A. Miller, John A. Ogren, Joyce E. Penner, Philip J. Rasch, Stephen E. Schwartz, John H. Seinfeld, Graeme L. Stephens, Omar Torres, Larry D. Travis, Bruce A . Wielicki, and Bin Yu

Aerosols exert myriad influences on the earth's environment and climate, and on human health. The complexity of aerosol-related processes requires that information gathered to improve our understanding of climate change must originate from multiple sources, and that effective strategies for data integration need to be established. While a vast array of observed and modeled data are becoming available, the aerosol research community currently lacks the necessary tools and infrastructure to reap maximum scientific benefit from these data. Spatial and temporal sampling differences among a diverse set of sensors, nonuniform data qualities, aerosol mesoscale variabilities, and difficulties in separating cloud effects are some of the challenges that need to be addressed. Maximizing the longterm benefit from these data also requires maintaining consistently well-understood accuracies as measurement approaches evolve and improve. Achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the earth system can be achieved only through a multidisciplinary, interagency, and international initiative capable of dealing with these issues. A systematic approach, capitalizing on modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies, can provide the necessary machinery to support this objective. We outline a framework for integrating and interpreting observations and models, and establishing an accurate, consistent, and cohesive long-term record, following a strategy whereby information and tools of progressively greater sophistication are incorporated as problems of increasing complexity are tackled. This concept is named the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON). To encompass the breadth of the effort required, we present a set of recommendations dealing with data interoperability; measurement and model integration; multisensor synergy; data summarization and mining; model evaluation; calibration and validation; augmentation of surface and in situ measurements; advances in passive and active remote sensing; and design of satellite missions. Without an initiative of this nature, the scientific and policy communities will continue to struggle with understanding the quantitative impact of complex aerosol processes on regional and global climate change and air quality.

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