Search Results

You are looking at 21 - 30 of 33 items for

  • Author or Editor: Arnold Gruber x
  • All content x
Clear All Modify Search
Hai-Tien Lee, Arnold Gruber, Robert G. Ellingson, and Istvan Laszlo

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) outgoing longwave radiation (OLR) product, which NOAA has been operationally generating since 1979, is a very long data record that has been used in many applications, yet past studies have shown its limitations and several algorithm-related deficiencies. Ellingson et al. have developed the multispectral algorithm that largely improved the accuracy of the narrowband-estimated OLR as well as eliminated the problems in AVHRR. NOAA has been generating High Resolution Infrared Radiation Sounder (HIRS) OLR operationally since September 1998. In recognition of the need for a continuous and long OLR data record that would be consistent with the earth radiation budget broadband measurements in the National Polar-orbiting Operational Environmental Satellite System (NPOESS) era, and to provide a climate data record for global change studies, a vigorous reprocessing of the HIRS radiance for OLR derivation is necessary.

This paper describes the development of the new HIRS OLR climate dataset. The HIRS level 1b data from the entire Television and Infrared Observation Satellite N-series (TIROS-N) satellites have been assembled. A new radiance calibration procedure was applied to obtain more accurate and consistent HIRS radiance measurements. The regression coefficients of the HIRS OLR algorithm for all satellites were rederived from calculations using an improved radiative transfer model. Intersatellite calibrations were performed to remove possible discontinuity in the HIRS OLR product from different satellites. A set of global monthly diurnal models was constructed consistent with the HIRS OLR retrievals to reduce the temporal sampling errors and to alleviate an orbital-drift-induced artificial trend. These steps significantly improved the accuracy, continuity, and uniformity of the HIRS monthly mean OLR time series. As a result, the HIRS OLR shows a comparable stability as in the Earth Radiation Budget Satellite (ERBS) nonscanner OLR measurements.

HIRS OLR has superb agreement with the broadband observations from Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth’s Radiant Energy System (CERES) in the ENSO-monitoring regions. It shows compatible ENSO-monitoring capability with the AVHRR OLR. Globally, HIRS OLR agrees with CERES with an accuracy to within 2 W m−2 and a precision of about 4 W m−2. The correlation coefficient between HIRS and CERES global monthly mean is 0.997. Regionally, HIRS OLR agrees with CERES to within 3 W m−2 with precisions better than 3 W m−2 in most places. HIRS OLR could be used for constructing climatology for applications that plan to use NPOESS ERBS and previously used AVHRR OLR observations. The HIRS monthly mean OLR data have high accuracy and precision with respect to the broadband observations of ERBE and CERES. It can be used as an independent validation data source. The uniformity and continuity of HIRS OLR time series suggest that it could be used as a reliable transfer reference for the discontinuous broadband measurements from ERBE, CERES, and ERBS.

Full access
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.

Full access
Patrick Market, Stacy Allen, Roderick Scofield, Robert Kuligowski, and Arnold Gruber

Abstract

The precipitation efficiencies for mesoscale convective systems (MCS) over the central United States are calculated. During July–September 2000 and June–September 2001, 24 MCS for which sufficient data were available occurred over or near Missouri. Precipitable water fields from the hourly Rapid Update Cycle (RUC) and radar-derived precipitation grids are used to calculate the precipitation efficiency. Geostationary Operational Environmental Satellite soundings and RUC winds are used to assess the pre-MCS environment. Statistical analysis reveals that precipitation efficiency has a relatively strong positive correlation with the relative humidity in the layer between the surface and the lifting condensation level; significant negative correlations are found between the precipitation efficiency and both the convective inhibition and the environmental wind shear. The latter, inverse relationship between shear and precipitation efficiency supports the findings of previous investigators.

Full access
Arnold Gruber, Xiujuan Su, M. Kanamitsu, and J. Schemm

Two large-scale precipitation datasets, one produced by the Global Precipitation Climatology Project (GPCP) and the other by the Climate Prediction Center of the National Weather Service, and called Climate Prediction Center Merged Analysis of Precipitation (CMAP), were compared. Both datasets blend satellite and gauge estimates of precipitation. And while the latter has its heritage in the GPCP, different analysis procedures and some additional types of input data used by CMAP yielded different values. This study used the error characteristics of the data to assess the significance of the observed differences. Despite good spatial and temporal correlations between the two fields some of the observed differences were significant at the 95% level. These were traced to the use of some different input data such as the use by CMAP of atoll gauges in the tropical Pacific and gauges uncorrected for wetting evaporation and aerodynamic effects. The former impacts the tropical ocean rain amounts and the latter is particularly noticeable in the Northern Hemisphere land areas. Also, the application of these datasets to the validation of atmospheric general circulation models is discussed.

Full access
Robert G. Ellingson, David J. Yanuk, Hai-Tien Lee, and Arnold Gruber

Abstract

A new technique for estimating outgoing longwave radiation from observations on the NOAA operational satellites has been developed from a regression analysis of radiation model calculations. The technique consists of a weighted sum of radiance in but four intervals sensed by the High-resolution InfraRed Sounder (HIRS). The analysis shows that model outgoing flux may be reproduced to within ±2 W · m−2 rms, which is about a factor of 4 smaller than the rms error of the method used by NOAA to estimate flux from the AVHRR. The small errors suggest that the new technique holds the promise of eliminating the large systematic errors possible with the current NOAA technique. Additionally, the new technique often the possibility of directly relating flux changes to changes in atmospheric parameters.

Full access
Arnold Gruber, Robert Ellingson, Philip Ardanuy, Mitchell Weiss, S. K. Yang, and Sung Nam Oh

Comparisons have been made between estimates of the outgoing longwave radiation (OLR) at the top of the atmosphere derived from narrowband Advanced Very High Resolution Radiometer (AVHRR) and broadband Earth Radiation Budget Experiment (ERBE) scanning instruments. Four months of measurements are considered: April, July, and October 1985 and January 1986. Instantaneous comparisons (i.e., collocated in space and time) are considered.

In the former, regional, zonal, and global analyses are performed using collocated and coincident OLR estimates on a 2.5° latitude-longitude scale. In general, the two datasets are found to be in reasonably good agreement, with the mean state and fundamental variability in time and space captured by the two sets of measurements. However, systematic biases are observed between the two datasets, particularly over the subtropical oceans, the daytime deserts, and over snow-covered surfaces at the high latitudes. The monthly global bias between the two datasets (ERBE minus AVHRR) is between −1 and 2 Wm−2 during daytime, and between 4 and 7 Wm−2 during nighttime, while the rms differences range between 12 (June) and 15 (January) Wm−2.

Radiative transfer simulations show that these systematic errors may be attributed to limitations in the single-channel narrowband to broadband algorithm. Even though the results may be globally unbiased, regional biases result where particularly persistent conditions (e.g., trade wind inversion, subsidence over deserts) prevail.

Full access
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%.

Full access
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.

Full access
Elizabeth E. Ebert, Michael J. Manton, Philip A. Arkin, Richard J. Allam, Gary E. Holpin, and Arnold Gruber

Three algorithm intercomparison experiments have recently been conducted as part of the Global Precipitation Climatology Project with the goal of (a) assessing the skill of current satellite rainfall algorithms, (b) understanding the differences between them, and (c) moving toward improved algorithms. The results of these experiments are summarized and intercompared in this paper.

It was found that the skill of satellite rainfall algorithms depends on the regime being analyzed, with algorithms producing very good results in the tropical western Pacific and over Japan and its surrounding waters during summer, but relatively poor rainfall estimates over western Europe during late winter. Monthly rainfall was estimated most accurately by algorithms using geostationary infrared data, but algorithms using polar data [Advanced Very High Resolution Radiometer and Special Sensor Microwave/Imager (SSM/I)] were also able to produce good monthly rainfall estimates when data from two satellites were available. In most cases, SSM/I algorithms showed significantly greater skill than IR-based algorithms in estimating instantaneous rain rates.

Full access
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.

Full access