The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...

The NCEP/NCAR 40-Year Reanalysis Project

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet’s resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

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Global Consequences of Land Use

世界経済・社会統計 = World development indicators

Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

In this paper we investigate the impact of the Atmospheric Infra-Red Sounder (AIRS) temperature retrievals on data assimilation and the resulting forecasts using the four-dimensional Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and a reduced resolution version of the NCEP Global Forecast System (GFS).Our results indicate that the AIRS temperature retrievals have a significant and consistent positive impact in the Southern Hemispheric extratropics on both analyses and forecasts,which is found not only in the temperature field but also in other variables.In tropics and the Northern Hemispheric extratropics these impacts are smaller,but are still generally positive or neutral.

Improved analyses and forecasts with AIRS temperature retrievals using the local ensemble transform Kalman filter

Ensemble Kalman Filter Data Assimilation of TES Retrievals

Improving Tropical Cyclone Predictions by Assimilation of Satellite-Retrieved Precipitation with Gaussian Transformation

Abstract. Localization is an essential technique for ensemble-based
data assimilations (DAs) to reduce sampling errors due to limited ensembles. Unlike traditional distance-dependent localization, the correlation cutoff method (Yoshida and Kalnay, 2018; Yoshida, 2019) tends to localize the observation impacts based on their background error correlations. This method was initially proposed as a variable localization strategy for coupled systems, but it can also can be utilized extensively as a spatial localization. This study introduced and examined the feasibility of the correlation cutoff method as an alternative spatial localization with the local ensemble transform Kalman filter (LETKF) preliminary on the Lorenz (1996) model. We compared the accuracy of the distance-dependent and correlation-dependent localizations and extensively explored the potential of the hybrid localization strategies. Our results suggest that the correlation cutoff method can deliver comparable analysis to the traditional localization more efficiently and with a faster DA spin-up. These benefits would become even more pronounced under a more complicated model, especially when the ensemble and observation sizes are reduced.


Applying prior correlations for ensemble-based spatial localization

The goal of SODA is to reconstruct the historical physical (and eventually biogeochemical) history of the ocean since the beginning of the 20th century. As its name implies, the Simple Ocean Data Assimilation ocean/sea ice reanalysis (SODA) uses a simple architecture based on community standard codes with resolution chosen to match available data and the scales of motion that are resolvable. Agreement with direct measurements (to within observational error estimates) as well as unbiased statistics are expected. While SODA remains a university-based research project, an objective is to support potential users by providing a reliable, well-documented, source of seasonal climate time-scale ocean reanalysis to complement the atmospheric reanalyses available elsewhere (NOAA/EMC, NASA/GMAO, and ECMWF, for example).\n\n SODA3 (SODA Version 3) is the latest release of SODA. The model has been switched to GFDL MOM5/SIS1 with eddy permitting 0.25 degree by 0.25 degree by 50 level resolution (28 kilometers at the Equator down to less than 10 kilometers at polar latitudes), similar to the ocean component of the GFDL CM2.5 coupled climate model, and includes the same SIS1 active sea ice model. A number of improvements have been included in the sequential DA filter, but for many reanalyses SODA3 retains a pre-specified flow-dependent error covariance.\n\n One of the focuses for SODA3 has been to identify, quantify, and limit sources of bias. A major source of bias is in the forward model that predicts the evolution of the flow. A major (but not the only) source of model bias, in turn, is introduced through bias in the meteorological fluxes (heat, freshwater, and momentum). To address this problem SODA3 is an 'ensemble' reanalysis, the spread of whose members provides information about sensitivity to errors in surface forcing. Many of these ensemble members are driven by fluxes that have been bias-corrected.

Eugenia Kalnay

Papers

Improved analyses and forecasts with AIRS temperature retrievals using the local ensemble transform Kalman filter

Ensemble Kalman Filter Data Assimilation of TES Retrievals

Improving Tropical Cyclone Predictions by Assimilation of Satellite-Retrieved Precipitation with Gaussian Transformation

Applying prior correlations for ensemble-based spatial localization

SODA Project: SODA3 Ensemble Means and Standard Deviations