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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

AIRS

Improving Weather Forecasting and Providing New Data on Greenhouse Gases

MOUSTAFA T. CHAHINE
,
THOMAS S. PAGANO
,
HARTMUT H. AUMANN
,
ROBERT ATLAS
,
CHRISTOPHER BARNET
,
JOHN BLAISDELL
,
LUKE CHEN
,
MURTY DIVAKARLA
,
ERIC J. FETZER
,
MITCH GOLDBERG
,
CATHERINE GAUTIER
,
STEPHANIE GRANGER
,
SCOTT HANNON
,
FREDRICK W. IRION
,
RAMESH KAKAR
,
EUGENIA KALNAY
,
BJORN H. LAMBRIGTSEN
,
SUNG-YUNG LEE
,
JOHN Le MARSHALL
,
W. WALLACE MCMILLAN
,
LARRY MCMILLIN
,
EDWARD T. OLSEN
,
HENRY REVERCOMB
,
PHILIP ROSENKRANZ
,
WILLIAM L. SMITH
,
DAVID STAELIN
,
L. LARRABEE STROW
,
JOEL SUSSKIND
,
DAVID TOBIN
,
WALTER WOLF
, and
LIHANG ZHOU

The Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAA's requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO2, CO, CH4, O3, SO2, and aerosols.

The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECMWF and the Met Office have also reported positive forecast impacts due AIRS.

AIRS is a hyperspectral sounder with 2,378 infrared channels between 3.7 and 15.4 μm. NOAA/NESDIS routinely distributes AIRS data within 3 hours to NWP centers around the world. The AIRS design represents a breakthrough in infrared space instrumentation with measurement stability and accuracies far surpassing any current research or operational sounder..The results we describe in this paper are “work in progress,” and although significant accomplishments have already been made much more work remains in order to realize the full potential of this suite of instruments.

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Sid A. Boukabara
,
Tong Zhu
,
Hendrik L. Tolman
,
Steve Lord
,
Steven Goodman
,
Robert Atlas
,
Mitch Goldberg
,
Thomas Auligne
,
Bradley Pierce
,
Lidia Cucurull
,
Milija Zupanski
,
Man Zhang
,
Isaac Moradi
,
Jason Otkin
,
David Santek
,
Brett Hoover
,
Zhaoxia Pu
,
Xiwu Zhan
,
Christopher Hain
,
Eugenia Kalnay
,
Daisuke Hotta
,
Scott Nolin
,
Eric Bayler
,
Avichal Mehra
,
Sean P. F. Casey
,
Daniel Lindsey
,
Louie Grasso
,
V. Krishna Kumar
,
Alfred Powell
,
Jianjun Xu
,
Thomas Greenwald
,
Joe Zajic
,
Jun Li
,
Jinliong Li
,
Bin Li
,
Jicheng Liu
,
Li Fang
,
Pei Wang
, and
Tse-Chun Chen

Abstract

In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies (S4). This O2R environment is critical for successful research-to-operations (R2O) transitions because it allows rigorous tracking, implementation, and merging of any changes necessary (to operational software codes, scripts, libraries, etc.) to achieve the scientific enhancement. So far, the S4 O2R environment, with close to 4,700 computing cores (60 TFLOPs) and 1,700-TB disk storage capacity, has been a great success and consequently was recently expanded to significantly increase its computing capacity. The objective of this article is to highlight some of the major achievements and benefits of this O2R approach and some lessons learned, with the ultimate goal of inspiring other O2R/R2O initiatives in other areas and for other applications.

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