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Robert G. Ellingson, David J. Yanuk, and Arnold Gruber

Abstract

The technique used by NOAA to estimate the outgoing longwave flux from 10 μm window radiance observations has been reexamined because the data that result from the application of the empirically determined regression equation are systematically lower than those obtained from regression models based on earlier radiative transfer calculations. A new set of radiation calculations was made from a set of 1600 atmospheric soundings and the resulting regression equation gives flux differences from the empirical model that are of the order of ±5 W m−2 over the range of 150 to 300 W m−2, as compared to the +10 W m−2 systematic differences from previous studies. The differences are attributed to the size and representativeness of the sample of soundings used in the radiation calculations.

The results also show that although the explained variance of the regression is of the order of 95%, this type of estimation technique may make errors of ±20 W m−2 or larger for a given radiance observation. This will lead to biased average flux estimates in geographical regions where the temperature and moisture profiles change little over extended time periods.

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Philip E. Ardanuy, Larry L. Stowe, Arnold Gruber, Mitchell Weiss, and Craig S. Long

Abstract

Collocated and coincident cloud and outgoing longwave radiation observations taken by experiments on board the Nimbus-7 satellite have been used to infer the daytime longwave cloud-radiative forcing. Through the specification of a time-series of daily values of cloud amount, cloud-top temperature, surface temperature, and outgoing longwave radiation, the clear-sky flux is obtained for both the summer (June, July, and August 1979) and winter (December 1979. January and February 1980) seasons. The longwave component of the cloud-radiative forcing is then computed by subtracting the observed outgoing longwave flux from the inferred clear-sky longwave flux. The results are compared to independent cloud forcing estimates produced using high spatial resolution radiometers and found to agree closely.

The resultant cloud forcing is analyzed regionally, zonally, and globally for each season to quantify, through observation, the role that clouds play in modulating the outgoing longwave radiation. The largest cloud forcing is found over regions of tropical convection, and reaches peak values of about 80 W m−2 in the vicinity of the summer and winter monsoon. Cloud forcing values of less than 10 W m−2 are evident over the deserts the subtropical oceans, and in the polar latitudes, Zonally, the cloud forcing reaches maxima over the Intertropical Convergence Zone (40 to 50 W m−2) and over the polar frontal zones of both hemispheres (25 to 30 W m−2), and minima in the subtropical belts and at the poles. Globally, the cloud forcing is found to be 24 W m−2. The globally averaged cloud cover for the same period is 50%.

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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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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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