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Robbie Iacovazzi
,
Quanhua “Mark” Liu
, and
Changyong Cao
Full access
John Xun Yang
,
Yalei You
,
William Blackwell
,
Cheng Da
,
Eugenia Kalnay
,
Christopher Grassotti
,
Quanhua (Mark) Liu
,
Ralph Ferraro
,
Huan Meng
,
Cheng-Zhi Zou
,
Shu-Peng Ho
,
Jifu Yin
,
Veljko Petkovic
,
Timothy Hewison
,
Derek Posselt
,
Antonia Gambacorta
,
David Draper
,
Sidharth Misra
,
Rachael Kroodsma
, and
Min Chen

Abstract

Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.

Open access
Suranjana Saha
,
Shrinivas Moorthi
,
Hua-Lu Pan
,
Xingren Wu
,
Jiande Wang
,
Sudhir Nadiga
,
Patrick Tripp
,
Robert Kistler
,
John Woollen
,
David Behringer
,
Haixia Liu
,
Diane Stokes
,
Robert Grumbine
,
George Gayno
,
Jun Wang
,
Yu-Tai Hou
,
Hui-ya Chuang
,
Hann-Ming H. Juang
,
Joe Sela
,
Mark Iredell
,
Russ Treadon
,
Daryl Kleist
,
Paul Van Delst
,
Dennis Keyser
,
John Derber
,
Michael Ek
,
Jesse Meng
,
Helin Wei
,
Rongqian Yang
,
Stephen Lord
,
Huug van den Dool
,
Arun Kumar
,
Wanqiu Wang
,
Craig Long
,
Muthuvel Chelliah
,
Yan Xue
,
Boyin Huang
,
Jae-Kyung Schemm
,
Wesley Ebisuzaki
,
Roger Lin
,
Pingping Xie
,
Mingyue Chen
,
Shuntai Zhou
,
Wayne Higgins
,
Cheng-Zhi Zou
,
Quanhua Liu
,
Yong Chen
,
Yong Han
,
Lidia Cucurull
,
Richard W. Reynolds
,
Glenn Rutledge
, and
Mitch Goldberg

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

Full access