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Christian D. Kummerow
,
David L. Randel
,
Mark Kulie
,
Nai-Yu Wang
,
Ralph Ferraro
,
S. Joseph Munchak
, and
Veljko Petkovic

Abstract

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.

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Arthur Y. Hou
,
Ramesh K. Kakar
,
Steven Neeck
,
Ardeshir A. Azarbarzin
,
Christian D. Kummerow
,
Masahiro Kojima
,
Riko Oki
,
Kenji Nakamura
, and
Toshio Iguchi

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events.

The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting.

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Arthur Y. Hou
,
Sara Q. Zhang
,
Arlindo M. da Silva
,
William S. Olson
,
Christian D. Kummerow
, and
Joanne Simpson

As a follow-on to the Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration in the United States, the National Space Development Agency of Japan, and the European Space Agency are considering a satellite mission to measure the global rainfall. The plan envisions an improved TRMM-like satellite and a constellation of eight satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-h intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and two Special Sensor Microwave/Imager (SSM/I) instruments.

Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. It is shown that assimilating the 6-h-averaged TMI and SSM/I surface rain rate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper-tropospheric moisture in the analysis produced by the Goddard Earth Observing System Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System instrument and brightness temperature observations by the Television Infrared Observational Satellite Operational Vertical Sounder instruments.

Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the Tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the Tropics.

These results were obtained using a variational procedure based on a 6-h time integration of a column model of moist physics with prescribed dynamical and other physical tendencies. The procedure estimates moisture tendency corrections at observation locations by minimizing the least square differences between the observed TPW and rain rates and those generated by the column model over a 6-h analysis window. These tendency corrections are then applied during the assimilation cycle to compensate for errors arising from both initial conditions and deficiencies in model physics. Our results point to the importance of addressing deficiencies in model physics in assimilating data types such as precipitation, for which the forward model based on convective parameterizations may have significant systematic errors.

This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of four-dimensional global datasets for climate analysis and weather forecasting applications.

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William S. Olson
,
Christian D. Kummerow
,
Song Yang
,
Grant W. Petty
,
Wei-Kuo Tao
,
Thomas L. Bell
,
Scott A. Braun
,
Yansen Wang
,
Stephen E. Lang
,
Daniel E. Johnson
, and
Christine Chiu

Abstract

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

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Satellite Data Simulator Unit

A Multisensor, Multispectral Satellite Simulator Package

Hirohiko Masunaga
,
Toshihisa Matsui
,
Wei-kuo Tao
,
Arthur Y. Hou
,
Christian D. Kummerow
,
Teruyuki Nakajima
,
Peter Bauer
,
William S. Olson
,
Miho Sekiguchi
, and
Takashi Y. Nakajima
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Toshihisa Matsui
,
Takamichi Iguchi
,
Xiaowen Li
,
Mei Han
,
Wei-Kuo Tao
,
Walter Petersen
,
Tristan L'Ecuyer
,
Robert Meneghini
,
William Olson
,
Christian D. Kummerow
,
Arthur Y. Hou
,
Mathew R. Schwaller
,
Erich F. Stocker
, and
John Kwiatkowski
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