An improved estimate of daily precipitation from the ERA5 reanalysis

Precipitation is an essential climate variable and a fundamental part of theglobal water cycle. Given its importance to society, precipitation is oftenassessed in climate monitoring activities, such as in those led by the Coperni-cus Climate Change Service (C3S). To undertake these activities, C3S predomi-nantly uses ERA5 reanalysis precipitation. Research has shown that short-range forecasts for precipitation made from this reanalysis can provide valu-able estimates of the actual (observed) precipitation in extratropical regionsbut can be less useful in the tropics. While some of these limitations will bereduced with future reanalyses because of the latest advancements, there ispotentially a more immediate way to improve the precipitation estimate.This is to use the precipitation modelled in the Four-Dimensional Variational(4D-Var) data assimilation window of the reanalysis, and it is the aim of thisstudy to evaluate this approach. Using observed 24-h precipitation accumula-tions at 5637 stations from 2001 to 2020, results show that smaller root-mean-square errors (RMSEs) and mean absolute errors are generally foundby using the ERA5 4D-Var precipitation. For example, for all available daysfrom 2001 to 2020, 87.5% of stations have smaller RMSEs. These improvementsare driven by reduced random errors in the 4D-Var precipitation because it isbetter constrained by observations, which are themselves sensitive to orinfluence precipitation. However, there are regions (e.g., Europe) where largerbiases occur, and via the decomposition of the Stable Equitable Error inProbability Space score, this is shown to be because the 4D-Var precipitationhas a wetter bias on ‘dry’ days than the standard ERA5 short-range forecasts.The findings also highlight that the 4D-Var precipitation does improve thediscrimination of ‘heavy’ observed events. In conclusion, an improved ERA5precipitation estimate is largely obtainable, and these results could proveuseful for C3S activities and for future reanalyses, including ERA

WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies

The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5 spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020). © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License

On the exchange of momentum over the open ocean

Author Posting. © American Meteorological Society, 2013. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 43 (2013): 1589–1610, doi:10.1175/JPO-D-12-0173.1.This study investigates the exchange of momentum between the atmosphere and ocean using data collected from four oceanic field experiments. Direct covariance estimates of momentum fluxes were collected in all four experiments and wind profiles were collected during three of them. The objective of the investigation is to improve parameterizations of the surface roughness and drag coefficient used to estimate the surface stress from bulk formulas. Specifically, the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk flux algorithm is refined to create COARE 3.5. Oversea measurements of dimensionless shear are used to investigate the stability function under stable and convective conditions. The behavior of surface roughness is then investigated over a wider range of wind speeds (up to 25 m s−1) and wave conditions than have been available from previous oversea field studies. The wind speed dependence of the Charnock coefficient α in the COARE algorithm is modified to , where m = 0.017 m−1 s and b = −0.005. When combined with a parameterization for smooth flow, this formulation gives better agreement with the stress estimates from all of the field programs at all winds speeds with significant improvement for wind speeds over 13 m s−1. Wave age– and wave slope–dependent parameterizations of the surface roughness are also investigated, but the COARE 3.5 wind speed–dependent formulation matches the observations well without any wave information. The available data provide a simple reason for why wind speed–, wave age–, and wave slope–dependent formulations give similar results—the inverse wave age varies nearly linearly with wind speed in long-fetch conditions for wind speeds up to 25 m s−1.This work was funded by the National Science Foundation Grant OCE04-24536 as part of the CLIVAR Mode Water Dynamics Experiment (CLIMODE) and the Office of Naval Research Grant N00014-05-1-0139 as part of the CBLAST-LOW program.2014-02-0

WFDE5: bias adjusted ERA5 reanalysis data for impact studies

The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5o spatial resolution, but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components as analyzed in an uncalibrated hydrological model (WaterGAP) than use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to re-generate part of the dataset or apply the same approach on other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole 2016 year, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60

Current challenges and future directions in data assimilation and reanalysis

The first Joint WCRP1-WWRP2 Symposium on Data Assimilation and Reanalysis took place on13-17 September 2021, and it was organized in conjunction with the ECMWF Annual Seminaron observations. The last WCRP/WWRP-organized meetings were held separately for data assimilation and reanalysis in 2017 (Buizza et al. 2018; Cardinali et al. 2019). Since then, commonchallenges and new emerging topics have increased the need to bring these communities together toexchange new ideas and findings. Thus, a symposium involving the aforementioned communitieswas jointly organized by DWD3, HErZ4, WCRP, WWRP, and the ECMWF annual seminar. Majorgoals were to increase diversity, provide early career scientists with opportunities to present theirwork and extend their professional network, and bridge gaps between the various communities.The online format allowed more than 500 participants from over 50 countries to meet in avirtual setting, using the gathertown 5 platform as the central tool to access the meeting. A virtualconference center was created where people could freely move around and talk to other close-byparticipants. A lobby served as the main hub and it connected the poster halls and the conferencerooms for the oral presentations and the ECMWF seminar talks. The feedback from the participantswas overwhelmingly positive.Scientifically, the meeting offered opportunities to bring together the communities of Earth systemdata assimilation, reanalysis and observations to identify current challenges, seek opportunitiesfor collaboration, and strategic planning on more integrated systems for the longer term. Thecontributions totalled 140 oral and over 150 poster presentations covering a large variety oftopics with increased interest in Earth system approaches, machine learning and increased spatial resolutions. Key findings of the symposium and the ECMWF annual seminar are summarized insection 2. Section 3 highlights the common and emerging challenges of these communities.Fil: Valmassoi, Arianna. Hans-ertel-centre For Weather Research; Alemania. Institut Fur Geowissenschaften ; Universitaet Bonn;Fil: Keller, Jan D.. Deutscher Wetterdienst; AlemaniaFil: Kleist, Daryl T.. National Ocean And Atmospheric Administration; Estados UnidosFil: English, Stephen. European Center For Medium Range Weather Forecasting; Reino UnidoFil: Ahrens, Bodo. Goethe Universitat Frankfurt; AlemaniaFil: Ďurán, Ivan Bašták. Goethe Universitat Frankfurt; AlemaniaFil: Bauernschubert, Elisabeth. Deutscher Wetterdienst; AlemaniaFil: Bosilovich, Michael G.. National Aeronautics and Space Administration; Estados UnidosFil: Fujiwara, Masatomo. Hokkaido University; JapónFil: Hersbach, Hans. European Center For Medium Range Weather Forecasting; Reino UnidoFil: Lei, Lili. Nanjing University; ChinaFil: Löhnert, Ulrich. University Of Cologne; AlemaniaFil: Mamnun, Nabir. Helmholtz Centre for Environmental Research; AlemaniaFil: Martin, Cory R.. German Research Centre for Geosciences; AlemaniaFil: Moore, Andrew. California State University; Estados UnidosFil: Niermann, Deborah. Deutscher Wetterdienst; AlemaniaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Scheck, Leonhard. Deutscher Wetterdienst; Alemani

The International Surface Pressure Databank version 2

The International Surface Pressure Databank (ISPD) is the world's largest collection of global surface and sea-level pressure observations. It was developed by extracting observations from established international archives, through international cooperation with data recovery facilitated by the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, and directly by contributing universities, organizations, and countries. The dataset period is currently 1768–2012 and consists of three data components: observations from land stations, marine observing systems, and tropical cyclone best track pressure reports. Version 2 of the ISPD (ISPDv2) was created to be observational input for the Twentieth Century Reanalysis Project (20CR) and contains the quality control and assimilation feedback metadata from the 20CR. Since then, it has been used for various general climate and weather studies, and an updated version 3 (ISPDv3) has been used in the ERA-20C reanalysis in connection with the European Reanalysis of Global Climate Observations project (ERA-CLIM). The focus of this paper is on the ISPDv2 and the inclusion of the 20CR feedback metadata. The Research Data Archive at the National Center for Atmospheric Research provides data collection and access for the ISPDv2, and will provide access to future versions

CMOD5.n - the CMOD5 GMF for neutral winds

13 pages, 6 figures, 1 appendixAt the OSI SAF 3rd user workshop in Amsterdam (September 2007), several users requested the OSI SAF to produce neutral winds rather than real 10m winds. Currently, the CMOD5 Geophysical Model Function (GMF) [3] is used to retrieve ASCAT winds, but it is modified such that 0.5 m/s is added to the wind speeds. This is done since we know from ERS scatterometer wind statistics that CMOD5 winds underestimate the real 10m winds by approximately 0.5 m/s [4]. The statistical difference between the current CMOD5 + 0.5 and the CMOD5.n (neutral) winds is another 0.2 m/s in all conditions studied [2,3]. As such, the OSI SAF plans to add 0.2 m/s to their ASCAT winds by implementing CMOD5.n. Following the above recommendation, ECMWF has fitted new CMOD5.n coefficients according to a desired CMOD5 + 0.7 m/s behaviour [2]. KNMI subsequently produced a CMOD5.n Lookup Table and tested the retrieved Maximum Likelihood Estimator (MLE) and winds against the CMOD5 + 0.7 m/s, hereafter referred to as CMOD5.7, retrieval outputs. This report describes the validation of the CM OD5.n coefficients and shows that the winds retrieved with the new GMF closely compare to those retrieved by CMOD5.7Peer Reviewe

Upper-Air observations from the German atlantic expedition (1925-27) and comparison with the twentieth century and ERA-20C reanalyses

Between April 1925 and June 1927, the research vessel Meteor cruised the tropical and southern Atlantic Ocean in the framework of the German Atlantic Expedition. One purpose was to systematically explore the vertical structure of the atmosphere. To this end, the ocean was crossed in 14 profiles across parallels of latitude. 801 pilot balloons and 217 kites were launched. The resulting data have been digitised in the framework of the European project ERA-CLIM. Here, they are compared to the Twentieth Century (20CR) and ERA-20C reanalyses, independent datasets based on the assimilation of synoptic pressure and hurricane pressure records, and marine surface winds for the latter, using monthly sea surface temperature and sea ice as boundary conditions. Both reanalyses display similar patterns of systematic differences relative to the observations for temperature, specific humidity, and wind. Furthermore, 20CR and ERA-20C show generally comparable anomaly correlations for all parameters, with the highest values found for pressure and temperature. In the southern extratropics, high (> 0.75) anomaly correlations are found for pressure in both 20CR and ERA-20C, and for temperature in 20CR. Medium (> 0.5) anomaly correlations are found for specific humidity in 20CR. Moderate anomaly correlations (> 0.44) are found for meridional wind in both 20CR and ERA-20C, and for temperature in ERA-20C. In contrast, low anomaly correlations (< 0.44) are found for zonal wind both in 20CR and ERA-20C, and for specific humidity in ERA-20C. In the Tropics, low anomaly correlations are found for all parameters except for pressure, which shows medium anomaly correlations for both 20CR and ERA-20C, and for meridional wind, which shows moderate anomaly correlations for 20CR. In all regions, both reanalyses strongly underestimate the observed range of zonal and particularly meridional wind variability. Even though remaining errors in the observational data cannot be excluded, we estimate that the inherent observational uncertainties do not alter our conclusions. Vice versa, two pieces of evidence support the credibility of the early upper-air data: the robust regressions of both 20CR and ERA-20C against observed pressure and temperature over a large spatial and temporal range, and the similarity between the uncertainties predicted by 20CR and the actual uncertainties determined from the root mean square difference of reanalysis and observation values.ISSN:0941-2948ISSN:1610-122