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Journal ArticleDOI

The JRA-55 Reanalysis: General Specifications and Basic Characteristics

About: This article is published in Journal of the Meteorological Society of Japan.The article was published on 2015-01-01 and is currently open access. It has received 3239 citations till now. The article focuses on the topics: Meteorological reanalysis & NCEP/NCAR Reanalysis.
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8,699 citations


Cites background or methods from "The JRA-55 Reanalysis: General Spec..."

  • ...At 3 hPa, JRA-55 and ERA-Interim show remarkable agreement throughout the period while the marked drop in MERRA-2 temperature in 2004 is an artefact known to be related to the assimilation of Aura MSL temperature profiles (Gelaro et al., 2017; Long et al., 2017)....

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  • ...Long-term variations from ERA5 are generally closer to those from GPCC and GPCP than are those from ERA-Interim and JRA-55....

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  • ...Agreement is lower overall between JRA-55 and the four other datasets for the same reason already mentioned in the previous paragraph (no direct ozone DA)....

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  • ..., 2017) from the NASA GMAO, JRA-55 (Kobayashi et al., 2015) produced by the JMA and CFSR (version 2) produced by NCEP (Saha et al....

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  • ...Beyond ECMWF, several groups produce global atmospheric reanalyses and the most recent products are the MERRA-2 reanalysis (Gelaro et al., 2017) from the NASA GMAO, JRA-55 (Kobayashi et al., 2015) produced by the JMA and CFSR (version 2) produced by NCEP (Saha et al., 2014)....

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Journal ArticleDOI
TL;DR: An overview of the MERRA-2 system and various performance metrics is provided, including the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes.
Abstract: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of M...

4,524 citations


Cites background from "The JRA-55 Reanalysis: General Spec..."

  • ...…and the Japan Meteorological Agency (JMA) provide a rich ensemble of climate data products beginning more or less with the period of regular conventional and satellite observations in themid to late twentieth century (Saha et al. 2010; Dee et al. 2011; Rienecker et al. 2011; Kobayashi et al. 2015)....

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Journal ArticleDOI
Pierre Friedlingstein1, Pierre Friedlingstein2, Michael O'Sullivan2, Matthew W. Jones3, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters4, Wouter Peters5, Julia Pongratz6, Julia Pongratz7, Stephen Sitch1, Corinne Le Quéré3, Josep G. Canadell8, Philippe Ciais9, Robert B. Jackson10, Simone R. Alin11, Luiz E. O. C. Aragão12, Luiz E. O. C. Aragão1, Almut Arneth, Vivek K. Arora, Nicholas R. Bates13, Nicholas R. Bates14, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp15, Selma Bultan7, Naveen Chandra16, Naveen Chandra17, Frédéric Chevallier9, Louise Chini18, Wiley Evans, Liesbeth Florentie4, Piers M. Forster19, Thomas Gasser20, Marion Gehlen9, Dennis Gilfillan, Thanos Gkritzalis21, Luke Gregor22, Nicolas Gruber22, Ian Harris23, Kerstin Hartung7, Kerstin Hartung24, Vanessa Haverd8, Richard A. Houghton25, Tatiana Ilyina6, Atul K. Jain26, Emilie Joetzjer27, Koji Kadono28, Etsushi Kato, Vassilis Kitidis29, Jan Ivar Korsbakken, Peter Landschützer6, Nathalie Lefèvre30, Andrew Lenton31, Sebastian Lienert32, Zhu Liu33, Danica Lombardozzi34, Gregg Marland35, Nicolas Metzl30, David R. Munro36, David R. Munro11, Julia E. M. S. Nabel6, S. Nakaoka17, Yosuke Niwa17, Kevin D. O'Brien11, Kevin D. O'Brien37, Tsuneo Ono, Paul I. Palmer, Denis Pierrot38, Benjamin Poulter, Laure Resplandy39, Eddy Robertson40, Christian Rödenbeck6, Jörg Schwinger, Roland Séférian27, Ingunn Skjelvan, Adam J. P. Smith3, Adrienne J. Sutton11, Toste Tanhua41, Pieter P. Tans11, Hanqin Tian42, Bronte Tilbrook43, Bronte Tilbrook31, Guido R. van der Werf44, N. Vuichard9, Anthony P. Walker45, Rik Wanninkhof38, Andrew J. Watson1, David R. Willis23, Andy Wiltshire40, Wenping Yuan46, Xu Yue47, Sönke Zaehle6 
University of Exeter1, École Normale Supérieure2, Norwich Research Park3, Wageningen University and Research Centre4, University of Groningen5, Max Planck Society6, Ludwig Maximilian University of Munich7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, Japan Agency for Marine-Earth Science and Technology16, National Institute for Environmental Studies17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

1,764 citations


Cites background or methods from "The JRA-55 Reanalysis: General Spec..."

  • ...The combination of CRU monthly data with 6-hourly forcing from JRA-55 (Kobayashi et al., 2015) is performed with methodology used in previous years (Viovy, 2016) adapted to the specifics of the JRA-55 data....

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  • ...The DGVM model runs were forced by either the merged monthly Climate Research Unit (CRU) and 6-hourly Japanese 55-year Reanalysis (JRA-55) data set or by the monthly CRU data set, both providing observation-based temperature, precipitation, and incoming surface radiation on a 0.5◦× 0.5◦ grid and updated to 2019 (Harris and Jones, 2019; Harris et al., 2020)....

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  • ...h See Zaehle and Friend (2010) and Krinner et al. (2005). i See also Woodward and Lomas (2004). j See also Ito and Inatomi (2012)....

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  • ...h See Zaehle and Friend (2010) and Krinner et al. (2005). i See also Woodward and Lomas (2004)....

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Journal ArticleDOI
TL;DR: The construction of a major new version of CRU TS (Climatic Research Unit gridded Time Series), updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually.
Abstract: CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901-2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.

1,689 citations

Journal ArticleDOI
Corinne Le Quéré1, Robbie M. Andrew, Pierre Friedlingstein2, Stephen Sitch2, Judith Hauck3, Julia Pongratz4, Julia Pongratz5, Penelope A. Pickers1, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell6, Almut Arneth7, Vivek K. Arora, Leticia Barbero8, Leticia Barbero9, Ana Bastos4, Laurent Bopp10, Frédéric Chevallier11, Louise Chini12, Philippe Ciais11, Scott C. Doney13, Thanos Gkritzalis14, Daniel S. Goll11, Ian Harris1, Vanessa Haverd6, Forrest M. Hoffman15, Mario Hoppema3, Richard A. Houghton16, George C. Hurtt12, Tatiana Ilyina5, Atul K. Jain17, Truls Johannessen18, Chris D. Jones19, Etsushi Kato, Ralph F. Keeling20, Kees Klein Goldewijk21, Kees Klein Goldewijk22, Peter Landschützer5, Nathalie Lefèvre23, Sebastian Lienert24, Zhu Liu1, Zhu Liu25, Danica Lombardozzi26, Nicolas Metzl23, David R. Munro27, Julia E. M. S. Nabel5, Shin-Ichiro Nakaoka28, Craig Neill29, Craig Neill30, Are Olsen18, T. Ono, Prabir K. Patra31, Anna Peregon11, Wouter Peters32, Wouter Peters33, Philippe Peylin11, Benjamin Pfeil34, Benjamin Pfeil18, Denis Pierrot9, Denis Pierrot8, Benjamin Poulter35, Gregor Rehder36, Laure Resplandy37, Eddy Robertson19, Matthias Rocher11, Christian Rödenbeck5, Ute Schuster2, Jörg Schwinger34, Roland Séférian11, Ingunn Skjelvan34, Tobias Steinhoff38, Adrienne J. Sutton39, Pieter P. Tans39, Hanqin Tian40, Bronte Tilbrook29, Bronte Tilbrook30, Francesco N. Tubiello41, Ingrid T. van der Laan-Luijkx32, Guido R. van der Werf42, Nicolas Viovy11, Anthony P. Walker15, Andy Wiltshire19, Rebecca Wright1, Sönke Zaehle5, Bo Zheng11 
University of East Anglia1, University of Exeter2, Alfred Wegener Institute for Polar and Marine Research3, Ludwig Maximilian University of Munich4, Max Planck Society5, Commonwealth Scientific and Industrial Research Organisation6, Karlsruhe Institute of Technology7, Cooperative Institute for Marine and Atmospheric Studies8, Atlantic Oceanographic and Meteorological Laboratory9, École Normale Supérieure10, Centre national de la recherche scientifique11, University of Maryland, College Park12, University of Virginia13, Flanders Marine Institute14, Oak Ridge National Laboratory15, Woods Hole Research Center16, University of Illinois at Urbana–Champaign17, Geophysical Institute, University of Bergen18, Met Office19, University of California, San Diego20, Utrecht University21, Netherlands Environmental Assessment Agency22, University of Paris23, Oeschger Centre for Climate Change Research24, Tsinghua University25, National Center for Atmospheric Research26, Institute of Arctic and Alpine Research27, National Institute for Environmental Studies28, Cooperative Research Centre29, Hobart Corporation30, Japan Agency for Marine-Earth Science and Technology31, Wageningen University and Research Centre32, University of Groningen33, Bjerknes Centre for Climate Research34, Goddard Space Flight Center35, Leibniz Institute for Baltic Sea Research36, Princeton University37, Leibniz Institute of Marine Sciences38, National Oceanic and Atmospheric Administration39, Auburn University40, Food and Agriculture Organization41, VU University Amsterdam42
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: . Accurate assessment of anthropogenic carbon dioxide ( CO2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions ( EFF ) are based on energy statistics and cement production data, while emissions from land use and land-use change ( ELUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate ( GATM ) is computed from the annual changes in concentration. The ocean CO2 sink ( SOCEAN ) and terrestrial CO2 sink ( SLAND ) are estimated with global process models constrained by observations. The resulting carbon budget imbalance ( BIM ), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ . For the last decade available (2008–2017), EFF was 9.4±0.5 GtC yr −1 , ELUC 1.5±0.7 GtC yr −1 , GATM 4.7±0.02 GtC yr −1 , SOCEAN 2.4±0.5 GtC yr −1 , and SLAND 3.2±0.8 GtC yr −1 , with a budget imbalance BIM of 0.5 GtC yr −1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr −1 . Also for 2017, ELUC was 1.4±0.7 GtC yr −1 , GATM was 4.6±0.2 GtC yr −1 , SOCEAN was 2.5±0.5 GtC yr −1 , and SLAND was 3.8±0.8 GtC yr −1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of + 2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr −1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models, originating outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding the global carbon cycle compared with previous publications of this data set (Le Quere et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018 .

1,458 citations


Cites methods from "The JRA-55 Reanalysis: General Spec..."

  • ...The DGVM model runs were forced by either the merged monthly CRU and 6-hourly JRA-55 data set or by the monthly CRU data set, both providing observation-based temperature, precipitation, and incoming surface radiation on a 0.5◦× 0.5◦ grid and updated to 2017 (Harris et al., 2014)....

    [...]

  • ...The combination of CRU monthly data with 6-hourly forcing is updated this year from NCEP to JRA-55 (Kobayashi et al., 2015), adapting the methodology used in previous years (Viovy, 2016) to the specifics of the JRA-55 data....

    [...]

  • ...MIROC Saeki and Patra (2017) Not applicable (not used in 2017). a The forcing for all DGVMs has been updated from CRUNCEP to CRU–JRA. b To account for the differences between the derivation of shortwave radiation (SWRAD) from CRU cloudiness and SWRAD from CRU–JRA-55, the photosynthesis scaling parameter αa was modified (−15...

    [...]

References
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Journal ArticleDOI
TL;DR: The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible, except that the horizontal resolution is T62 (about 210 km) as discussed by the authors.
Abstract: 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...

28,145 citations

Journal ArticleDOI
TL;DR: ERA-Interim as discussed by the authors is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which will extend back to the early part of the twentieth century.
Abstract: 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

22,055 citations

Journal ArticleDOI
TL;DR: ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions as mentioned in this paper.
Abstract: ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re-analysis period, with assimilable data provided by a succession of satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean-buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA-40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA-40. This benefited from many of the changes introduced into operational forecasting since the mid-1990s, when the systems used for the 15-year ECMWF re-analysis (ERA-15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized. A number of results indicative of the overall performance of the data assimilation system, and implicitly of the observing system, are presented and discussed. The comparison of background (short-range) forecasts and analyses with observations, the consistency of the global mass budget, the magnitude of differences between analysis and background fields and the accuracy of medium-range forecasts run from the ERA-40 analyses are illustrated. Several results demonstrate the marked improvement that was made to the observing system for the southern hemisphere in the 1970s, particularly towards the end of the decade. In contrast, the synoptic quality of the analysis for the northern hemisphere is sufficient to provide forecasts that remain skilful well into the medium range for all years. Two particular problems are also examined: excessive precipitation over tropical oceans and a too strong Brewer-Dobson circulation, both of which are pronounced in later years. Several other aspects of the quality of the re-analyses revealed by monitoring and validation studies are summarized. Expectations that the ‘second-generation’ ERA-40 re-analysis would provide products that are better than those from the firstgeneration ERA-15 and NCEP/NCAR re-analyses are found to have been met in most cases. © Royal Meteorological Society, 2005. The contributions of N. A. Rayner and R. W. Saunders are Crown copyright.

7,110 citations

Journal ArticleDOI
TL;DR: The numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence, and the convergence properties are studied to prove global convergence on uniformly convex problems.
Abstract: We study the numerical performance of a limited memory quasi-Newton method for large scale optimization, which we call the L-BFGS method. We compare its performance with that of the method developed by Buckley and LeNir (1985), which combines cycles of BFGS steps and conjugate direction steps. Our numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence. We show that the L-BFGS method can be greatly accelerated by means of a simple scaling. We then compare the L-BFGS method with the partitioned quasi-Newton method of Griewank and Toint (1982a). The results show that, for some problems, the partitioned quasi-Newton method is clearly superior to the L-BFGS method. However we find that for other problems the L-BFGS method is very competitive due to its low iteration cost. We also study the convergence properties of the L-BFGS method, and prove global convergence on uniformly convex problems.

7,004 citations

Journal ArticleDOI
TL;DR: The Global Precipitation Climatology Project (GPCP) version 2 Monthly Precise Analysis as discussed by the authors is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations.
Abstract: The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 degrees x 2.5 degrees latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The data set is extended back into the premicrowave era (before 1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the raingauge analysis. This monthly analysis is the foundation for the GPCP suite of products including those at finer temporal resolution, satellite estimate, and error estimates for each field. The 23-year GPCP climatology is characterized, along with time and space variations of precipitation.

4,951 citations

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