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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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C. F. Ropelewski, J. E. Janowiak, and M. S. Halpert

Abstract

No abstract available.

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J. E. Janowiak, C. F. Ropelewski, and M. S. Halpert

Abstract

An objective method to identify and track significant global precipitation anomalies on time scales of a month or longer is presented. The technique requires current observations of monthly precipitation amounts for each station and long term (20 or more years) monthly precipitation histories. Tests indicate that the technique compares favorably with the well-known Palmer Drought Severity Index (PDSI) and Crop Moisture Index (CMI) in the United States. Since monthly precipitation data are readily available in a near real-time framework, this method makes an automated, global precipitation anomaly monitoring system possible.

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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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L. E. Lucas, D. E. Waliser, P. Xie, J. E. Janowiak, and B. Liebmann

Abstract

Due to its long record length (approximately 25 years), the outgoing longwave radiation (OLR) dataset has been used in a multitude of climatological studies including studies on tropical circulation and convection, the El Niño–Southern Oscillation (ENSO) phenomenon, and the earth's radiation budget. Although many of the climatological studies using OLR have proven invaluable, proper interpretation of the low-frequency components of the data could be limited by the presence of biases introduced by changes in the satellite equatorial crossing time (ECT). Since long-term global changes could be masked or contaminated by this instrumental bias, it is necessary to take steps to ensure that the daily, global OLR dataset is free from such biases and is as accurate as possible.

The goal of this study is to derive a method for estimating the ECT biases in the daily, global OLR dataset. Our analysis utilizes a Procrustes targeted empirical orthogonal function rotation (REOF) on an interpolated OLR dataset to try to isolate and remove the two major ECT biases—afternoon satellite orbital drift and the abrupt transitions from a morning satellite to an afternoon satellite—from the dataset. Two targeted REOF analyses are performed to separate and distinguish between these two artificial satellite bias modes. A “common ECT” of approximately 0245 LST is established for the dataset by removing an estimate of these two ECT biases.

Results from the analysis indicate that changes in ECTs can cause large regional biases over both ocean and tropical landmasses. The afternoon satellite ECT drift-bias accounts for 0.4% of the pentad anomaly variance. During a single satellite series (e.g., NOAA-11), the afternoon drift-bias can introduce a difference as large as 10.5 W m−2 in the OLR values collected over most tropical landmasses. The morning to afternoon satellite transition bias accounts for 0.9% of the pentad anomaly variance, and is shown to cause a bias of 12 W m−2 in the OLR values over most tropical landmasses during the NOAA-SR satellite series. The data are corrected by removing a statistically derived synthetic eigenvector that is associated with each of the ECT bias modes. This synthetic eigenvector is used instead of the exact values of the satellite bias eigenvector to ensure that only the artificial variability is removed from the dataset.

The two REOF modes produced in this study are nearly orthogonal to each other having a correlation of only 0.17. This near orthogonality suggests that the use of the two-mode method presented in this study can more adequately describe the individual nature of each of the two ECT biases than a single REOF mode examined in previous studies. However, due to the presence of other forms of variability, it is likely that this study's estimate of the ECT bias includes ECT-related bias as well as some aspects of variability that may be associated with sensor changes, intersatellite calibration and/or natural climate variability. The strengths and limitations of the above technique are discussed, as are suggestions for future efforts.

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R. W. Higgins, Y. Yao, E. S. Yarosh, J. E. Janowiak, and K. C. Mo

Abstract

The influence of the Great Plains low-level jet (LLJ) on summertime precipitation and moisture transport over the central United States is examined in observations and in assimilated datasets recently produced by the NCEP/NCAR and the NASA/DAO. Intercomparisons between the assimilated datasets and comparisons with station observations of precipitation, winds, and specific humidity are used to evaluate the limitations of the assimilated products for studying the diurnal cycle of rainfall and the Great Plains LLJ. The winds from the reanalyses are used to diagnose the impact of the LLJ on observed nocturnal precipitation and moisture transport over a multisummer (JJA 1985–89) period. The impact of the LLJ on the overall moisture budget of the central United States is also examined.

An inspection of the diurnal cycle of precipitation in gridded hourly station observations for 1963–93 reveals a well-defined nocturnal maximum over the Great Plains region during the spring and summer months consistent with earlier observational studies. During summer in excess of 25% more precipitation falls during the nighttime hours than during the daytime hours over a large portion of the Great Plains, with a commensurate decrease in the percentage amount of nocturnal precipitation along the Gulf Coast. Inspection of the nighttime precipitation by month shows that the maximum in precipitation along the Gulf Coast slowly shifts northward from the lower Mississippi Valley to the upper Midwest during the late spring and summer months and then back again during the fall.

Both reanalyses produce a Great Plains LLJ with a structure, diurnal cycle, and frequency of occurrence that compares favorably to hourly wind profiler data. Composites of observed nighttime rainfall during LLJ events show a fundamentally different pattern in the distribution of precipitation compared to nonjet events. Overall, LLJ events are associated with enhanced precipitation over the north central United States and Great Plains and decreased precipitation along the Gulf Coast and East Coast; nonjet events are associated with much weaker anomalies that are generally in the opposite sense. Inspection of the LLJ composites for each month shows a gradual shift of the region of enhanced precipitation from the northern tier of states toward the south and east in a manner consistent with the anomalous moisture transport. LLJ-related precipitation is found to be associated most closely with the strongest, least frequent LLJ events.

The moisture transport in the reanalyses compares favorably to radiosonde data, although significant regional differences exist, particularly along the Gulf Coast during summer. The diurnal cycle of the low-level moisture transport is well resolved in the reanalyses with the largest and most extensive anomalies being those associated with the nocturnal inland flow of the Great Plains LLJ. Examination of the impact of the LLJ on the nighttime moisture transport shows a coherent evolution from May to August with a gradual increase in the anomalous westerly transport over the southeastern United States, consistent with the evolution of the precipitation patterns. The impact of the LLJ on the overall moisture budget during summer is considerable with low-level inflow from the Gulf of Mexico increasing by more than 45%, on average, over nocturnal mean values.

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John E. Janowiak, Arnold Gruber, C. R. Kondragunta, Robert E. Livezey, and George J. Huffman

Abstract

The Global Precipitation Climatology Project (GPCP) has released monthly mean estimates of precipitation that comprise gauge observations and satellite-derived precipitation estimates. Estimates of standard random error for each month at each grid location are also provided in this data release. One of the primary intended uses of this dataset is the validation of climatic-scale precipitation fields that are produced by numerical models. Nearly coincident with this dataset development, the National Centers for Environmental Prediction and the National Center for Atmospheric Research have joined in a cooperative effort to reanalyze meteorological fields from the present back to the 1940s using a fixed state-of-the-art data assimilation system and large input database.

In this paper, monthly accumulations of reanalysis precipitation are compared with the GPCP combined rain gauge–satellite dataset over the period 1988–95. A unique feature of this comparison is the use of standard error estimates that are contained in the GPCP combined dataset. These errors are incorporated into the comparison to provide more realistic assessments of the reanalysis model performance than could be attained by using only the mean fields. Variability on timescales from intraseasonal to interannual are examined between the GPCP and reanalysis precipitation. While the representation of large-scale features compares well between the two datasets, substantial differences are observed on regional scales. This result is not unexpected since present-day data assimilation systems are not designed to incorporate observations of precipitation. Furthermore, inferences of deficiencies in the reanalysis precipitation should not be projected to other fields in which observations have been assimilated directly into the reanalysis model.

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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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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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M. R. P. Sapiano, J. E. Janowiak, P. A. Arkin, H. Lee, T. M. Smith, and P. Xie

Abstract

The longest record of precipitation estimated from satellites is the outgoing longwave radiation (OLR) precipitation index (OPI), which is based on polar-orbiting infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) instrument that has flown onboard successive NOAA satellites. A significant barrier to the use of these data in studies of the climate of tropical precipitation (among other things) is the large bias caused by orbital drift that is present in the OLR data. Because the AVHRR instruments are deployed on the polar-orbiting spacecraft, OLR observations are recorded at specific times for each earth location for each day. Discontinuities are caused by the use of multiple satellites with different observing times as well as the orbital drift that occurs throughout the lifetime of each satellite. A regression-based correction is proposed based solely on the equator crossing time (ECT). The correction allows for separate means for each satellite as well as separate coefficients for each satellite ECT. The correction is calculated separately for each grid box but is applied only at locations where the correction is correlated with the OLR estimate. Thus, the correction is applied only where deemed necessary.

The OPI is used to estimate precipitation from the OLR estimates based on the new corrected version of the OLR, the uncorrected OLR, and two earlier published corrected versions. One of the earlier corrections is derived by removing variations from AVHRR based on EOFs that are identified as containing spurious variations related to the ECT bias, whereas the other is based on OLR estimates from the High Resolution Infrared Radiation Sounder (HIRS) that have been corrected using diurnal models for each grid box. The new corrected version is shown to be free of nearly all of the ECT bias and has the lowest root mean square difference when compared to gauges and passive microwave estimates of precipitation. The EOF-based correction fails to remove all of the variations related to the ECT bias, whereas the correction based on HIRS removes much of the bias but appears to introduce erroneous trends caused by the water vapor signal to which these data are sensitive. The new correction for AVHRR OLR works well in the tropics where the OPI has the most skill, but users should be careful when interpreting trends outside this region.

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