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The pixel size of the Infrared Atmospheric Sounding Interferometer (IASI) remote sensor is much smaller than the horizontal grid size of current Chemical Transport Models (CTMs). In order to assimilate the maximum of information from the IASI retrievals, we have increased the horizontal resolution of our model MOCAGE to be consistent with the IASI pixel size. Experiments are carried out with the Valentina data assimilation system using the standard and the high resolution versions of the model. Two resolutions of the horizontal Gaussian grid have been used for the model: with a T42 and a T170 triangular truncations. Our study is based on the combination of data from the IASI instrument and from the Microwave Limb Sounder (MLS), since this latter dataset allows the information to be spread through the whole atmospheric columns at a low computational cost. Two datasets of ozone super-observations have been constructed by averaging the IASI data on the two model grids. Direct model simulations without data assimilation first show that the increase of the horizontal resolution modifies the ozone smallest scale structures as well as the ozone meridional distribution. This modification results from a better representation of the vertical velocity with the T170 configuration. When the ozone assimilation is performed there is less influence of the horizontal resolution of the model. Nevertheless, in a general way, comparisons with independent data show large reductions of the ozone standard deviations when the resolution is increased. When the ozone assimilation is performed with the high resolution dataset, the high resolution model does not improve the ozone analysis compared to the one obtained with the same model resolution but with the low resolution IASI dataset. This result is due to the difficulty to combine IASI data and MLS data. For assimilating IASI data at high resolution the horizontal correlation length-scale has to be decreased to catch the small scale structures present in the dataset. By doing so the influence of the coarser resolution MLS data is decreased and part of the information brought on the vertical shape of the ozone profile is lost. It is concluded that it is essential to add information on the vertical distribution of ozone column when the IASI data is assimilated at a resolution close to the pixel size. Using IASI averaging kernels would likely improve the simulations, but the computational cost would be much higher. Alternatively, better results might be obtained by a careful tuning of the horizontal correlation length-scale.

Lettuce ring necrosis virus (LRNV), genus Ophiovirus, was detected by the Netherlands Institute for Vectors, Invasive plants and Plant health (NIVIP) in June and November of 2021 in two samples of chili pepper fruits (Capsicum spp.), both in mixed infection with other viruses. The first sample originated from a production site in Belgium (Sample ID: 40009704) and the second from a production site in the Netherlands (Sample ID: 41115269). One of the fruits of 40009704 showed a light purple circular pattern, while fruits from 41115269 showed colored (ring)spots. The samples were analyzed using Illumina sequencing on a NovaSeq 6000 platform (PE 150) as described previously (Hammond et al., 2021), obtaining 39.9M and 22.8M total reads for 40009704 and 41115269. The corresponding sequence read archives (SRA) were deposited in the NCBI SRA database under BioProject accession number PRJNA917231. From both samples, the nearly complete genome of LRNV (RNA1-4) was obtained and deposited in GenBank (40009704, OQ160823- OQ160826 (7616, 1799, 1502, 1382 nt, mapped reads: 40K, 12K, 114K, 12K , average read coverage (ARC): 0.8K, 0.9K, 11.3K and 1.1K); 41115269, OQ160827- OQ160830 (7616, 1801, 1518, 1389 nt, mapped reads: 112K, 7K, 357K, 55K reads, ARC: 2.2K, 0.6K, 34K and 5.8K)). The shared sequence identities with the Genbank reference sequence of LRNV (NC_006051-NC_006051) were 99.2 and 99.2% (RNA1), 99.1 and 99.1% (RNA2), 98.3 and 98.8% (RNA3), 99.0 and 98.9% (RNA4) for 40009704 and 41115269 respectively. The shared sequence identities between 40009704 and 41115269 were 99.9 (RNA1), 99.0 (RNA2), 99.1 (RNA3) and 99.5% (RNA4). In addition to LRNV, the ophiovirus ranunculus white mottle virus (RWMV) was detected in both samples (OQ160831-OQ160834; OQ160835-OQ160838), while the tobamovirus pepper mild mottle virus (PMMoV) was present in the fruits of 41115269 (OQ160839). Since RWMV has been associated with leaf symptoms in pepper (Gambley et al., 2019; Rivarez et al., 2022) and the colored (ring)spots of 41115269 were very similar to reported symptoms of PMMoV-infected pepper fruits (Martínez-Ochoa et al., 2003), it remains unclear whether LRNV contributed to the observed symptoms. Additionally, LRNV was detected in tomato (Solanum lycopersicum) in Belgium in 2020. In the frame of a metagenomic survey using Virion-Associated Nucleic Acids (VANA)-based protocol (Maclot et al., 2021) on a Nextseq 500 platform (PE 150), partial genome sequences of LRNV were detected in two pools of tomato plants. One pool was made of 44 asymptomatic cultivars from a non-commercial grower (one sample per cultivar) yielding 118K total reads of which 84, 59, 335, and 18 reads mapped on RNA1, 2, 3, and 4, covering 35%, 69%, 100% and 55% of the genome, respectively. The other pool consisted of 15 plants from one cultivar from a production site yielding 3.1M total reads of which 6 and 5 reads mapped on RNA3 and 4, respectively. The detection of LRNV was confirmed for both pooled samples using the real-time RT-PCR method, targeting the CP gene, as described by Maachi et al. (2021). To our knowledge this is the first report of LRNV in pepper anywhere in the world. Additionally, although the disease lettuce ring necrosis in lettuce (Lactuca sativa) has been described in Belgium and the Netherlands before the causal agent was identified (Bos & Huijberts, 1996), this is the first official report of this virus in Belgium and the Netherlands. This publication resulted from pre-publication data sharing of sequences and biological data among plant virologists to provide more context to two independent findings (Hammond et al., 2021). Bos, L., & Huijberts, N. (1996). Lettuce ring necrosis, caused by a chytrid-borne agent distinct from lettuce big-vein ‘virus’. European Journal of Plant Pathology, 102(9), 867-873. https://doi.org/10.1007/bf01877057 Gambley, C., Persley, D., & Thomas, J. E. (2019). First record of Ranunculus white mottle virus from Australia. New Disease Reports, 40(1), 13-13. https://doi.org/10.5197/j.2044-0588.2019.040.013 Hammond, J., Adams, I. P., Fowkes, A. R., McGreig, S., Botermans, M., Oorspronk, J. J. A., Westenberg, M., Verbeek, M., Dullemans, A. M., Stijger, C. C. M. M., Blouin, A. G., Massart, S., De Jonghe, K., Heyneman, M., Walsh, J. A., & Fox, A. (2021). Sequence analysis of 43‐year old samples of Plantago lanceolata show that Plantain virus X is synonymous with Actinidia virus X and is widely distributed. Plant Pathology, 70, 249-258. https://doi.org/10.1111/ppa.13310 Maachi, A., Torre, C., Sempere, R. N., Hernando, Y., Aranda, M. A., & Donaire, L. (2021). Use of High-Throughput Sequencing and Two RNA Input Methods to Identify Viruses Infecting Tomato Crops. Microorganisms, 9(5). https://doi.org/10.3390/microorganisms9051043 Maclot, F. J., Debue, V., Blouin, A. G., Fontdevila Pareta, N., Tamisier, L., Filloux, D., & Massart, S. (2021). Identification, molecular and biological characterization of two novel secovirids in wild grass species in Belgium. Virus Res, 298, 198397. https://doi.org/10.1016/j.virusres.2021.198397 Martínez-Ochoa, N., Langston, D. B., Mullis, S. W., & Flanders, J. T. (2003). First Report of Pepper mild mottle virus in Jalapeno Pepper in Georgia. Plant Health Progress, 4(1). https://doi.org/10.1094/php-2003-1223-01-hn Rivarez, M. P. S., Kogej, Z., Jakos, N., Pecman, A., Seljak, G., Vucurovic, A., Ravnikar, M., Mehle, N., & Kutnjak, D. (2022). First Report of Ranunculus White Mottle Ophiovirus in Slovenia in Pepper with Yellow Leaf Curling Symptom and in Tomato. Plant Dis, PDIS08211624PDN. https://doi.org/10.1094/PDIS-08-21-1624-PDN

With the emergence of high throughput sequencing (HTS) technologies, the discovery of new plant viruses has outpaced their biological characterization. However, it is crucial to understand the biology of these viruses to evaluate the risks they pose for the production of crops and natural ecosystems and to manage them properly. In 2018, Physostegia chlorotic mottle virus (PhCMoV) was detected in Austria in aPhysostegiaplant (Lamiaceae) using HTS, and subsequent prepublication data sharing associated the presence of the virus with severe fruit symptoms on important crops like tomato, eggplant, and cucumber across nine European countries. This discovery led to a collaborative effort to understand better the virus’s genetic diversity, host range, symptomatology, and distribution. Still, specific knowledge gaps remained. In this study, the authors address these gaps by examining the transmission mode, prevalence, and disease severity of PhCMoV. Bioassay and field survey confirmed the causal association between the presence of the virus and symptoms on tomato and eggplant. The investigation also mapped out the historical and geographic footprint of the virus, spanning back 30 years and including a new location, Switzerland. Based on field survey, PhCMoV was found to naturally infect 11 new host plant species across seven families, extending the host range of PhCMoV to 20 plant species across 14 plant families. Greenhouse assays with mechanical inoculation showed that yield losses could reach 100% depending on the phenological stage of the plant at the time of infection. The study also identified a polyphagous leafhopper species (Anaceratagalliasp.) as the natural vector of PhCMoV. PhCMoV was widespread in diversified vegetable farms in Belgium where tomato is grown in soil, occurring in approximately one-third of such farms. However, outbreaks were sporadic and it can be suggested that they were associated with specific cultural practices, such as the cultivation of perennial plants in tomato tunnels that can serve as a host for both the virus and its vector. To further explore this phenomenon and better manage the virus, studying the ecology of theAnaceratagalliaevector would be beneficial.

The number of small-scale diversified vegetable growers in industrialized countries has risen sharply over the last ten years. The risks associated with plant viruses in these systems have been barely studied in Europe, yet dramatic virus emergence events, such as tomato brown fruit rugose virus, sometimes occur. We developed a methodology that aimed to understand better the implications related to viruses for tomato production in Belgian’s vegetable farms by comparing growers’ perception of the presence of viral symptoms (visual inspection) with non targeting detection of nearly all viruses present in the plants by high throughput sequencing technologies (HTS). Virus presence and impact were interpreted considering the farm’s typology and cultural practices, the grower’s professional profiles, and visual inspection of plant-viral-like symptoms. Overall, The data indicated that most growers have limited understanding of tomato viruses and are not concerned about them. Field observations were correlated to this perception as the prevalence of symptomatic plants was usually lower than 1%. However, important and potentially emergent viruses, mainly transmitted by insects, were detected in several farms. Noteworthy, the presence of these viruses was correlated with the number of plant species grown per site (diversity) but not with a higher awareness of the growers regarding plant viral diseases or a higher number of symptomatic plants. In addition, both HTS and perception analysis underlined the rising incidence and importance of an emergent virus: Physostegia chlorotic mottle virus. Overall, the original methodology developed here, combining social science with HTS technologies, could be applied to other crops in other systems to identify emergent risks associated with plant viruses and can highlight the communication needed toward growers to mitigate epidemics.

Ensuring the reliability of diagnostic activities is an essential cornerstone of plant health strategies to reduce the risk of entry and spread of plant pests in a region and ultimately their impacts. Diagnostic tests should be validated to ensure that they are fit for purpose. Validation is usually done by diagnostic laboratories, although companies commercializing diagnostic kits also produce validation data for their products. Due to the high number of pest, matrix, and method combinations and given the significant resources required to validate tests, it is essential that validation data are shared with the entire diagnostic community and produced in a harmonized way to facilitate their use by different stakeholders. Indeed, the selection of tests to be used in specific contexts is not the sole responsibility of diagnostic laboratories but also involves national plant protection organizations. The VALITEST EU project (2018 to 2021) was established to tackle all these issues. New validation data for tests targeting important pests for the European and Mediterranean Plant Protection Organization region were produced. Guidelines to improve and harmonize the validation framework were developed. Sharing of validation data and experience was ensured through the development of new or existing databases, the organization of training courses, and the dissemination of the project outputs in scientific publications and standards. Finally, the involvement of researchers, diagnosticians, policy makers, inspectors, and industries and the establishment of the European Plant Diagnostic Industry Association were important actions to strengthen the interactions between plant health stakeholders. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

High-throughput sequencing (HTS) and sequence mining tools revolutionized virus detection and discovery in recent years and implementing them with classical plant virology techniques results to a powerful approach to characterize viruses. An example of a virus discovered through HTS is Solanum nigrum ilarvirus 1 (SnIV1) (familyBromoviridae), which was recently reported in various solanaceous plants from France, Slovenia, Greece, and South Africa. It was likewise detected in grapevines (Vitaceae) and severalFabaceaeandRosaceaeplant species. Such a very diverse host association is atypical for ilarviruses, thus warranted further investigation. In this study, modern and classical virological tools were combined to accelerate the characterization of SnIV1. Through HTS-based virome surveys, mining of sequence read archive datasets, and literature search, SnIV1 was further identified from diverse plant and non-plant sources globally. SnIV1 isolates showed relatively low variability compared to other phylogenetically related ilarviruses. Phylogenetic analyses showed a distinct basal clade of isolates from Europe, while the rest formed clades of mixed geographic origin. Furthermore, systemic infection of SnIV1 inSolanum villosumand its mechanical and graft transmissibility to solanaceous species were demonstrated. Near identical SnIV1 genomes from the inoculum (S. villosum) and inoculatedNicotiana benthamianawere sequenced, thus partially fulfilling Koch’s postulates. SnIV1 was shown to be seed-transmitted and potentially pollen-borne, has spherical virions, and possibly induces histopathological changes in infectedN. benthamianaleaf tissues. Overall, this study provided information to better understand the diversity, distribution, and pathobiology of SnIV1, but whether it could emerge as a destructive pathogen remains uncertain.FundingEU Horizon 2020 Marie Skłodowska-Curie Actions Innovative Training Network (H2020 MSCA-ITN) project no. GA 813542Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection and Slovenian Research Agency (ARRS) funding no. P4-0165, P4-0407, J4-4553Balik Scientist Program (Republic Act 11035) of the Department of Science and Technology– Philippine Council for Agriculture, Aquatic, and Natural Resources Research and Development (DOST–PCAARRD), Republic of the PhilippinesThe Belgian FPS Health Food Chain Safety and Environment under Project RT18/3 SEVIPLANT

The assimilation of clear-sky radiance in the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric analysis relies on the clear-sky radiances observation operator. Some of these radiances have frequencies that make them sensitive to both the surface and atmosphere. Because the atmospheric and surface analyses are currently not strongly coupled, a specific treatment of the surface is required. The observation operator specifically expects a skin temperature value at the observation location and time as well as the profiles of the atmospheric variables along the viewing path. This skin temperature is added to the control variable and optimised simultaneously with all of the atmospheric variables to produce optimal simulated radiances. We present two approaches to add the skin temperature to the control variable. In the current TOVS Control Variable (TOVSCV) approach, a series of skin temperature values per observation location is added to the control variable. Effectively, in the optimisation process, the skin temperature acts as a sink variable in observation space and is uncoupled from the skin temperature at other locations. In the novel SKin Temperature in the Extended Control Vector (SKTECV) approach, two-dimensional skin temperature fields are added to the control variable. All clear-sky radiances then participate in the optimisation of these two-dimensional fields, and the analysis produces temporally and spatially consistent skin temperature fields. We compare the two approaches over two seasons of 3 months each. Overall, there is a neutral impact of the new approach on the analysis and forecast. Moreover, there is some evidence that the contribution of the subsurface layers should be represented in the new approach for the skin temperature associated with the microwave instruments.

High-throughput sequencing (HTS) technologies have the potential to become one of the most significant advances in molecular diagnostics. Their use by researchers to detect and characterize plant pathogens and pests has been growing steadily for more than a decade and they are now envisioned as a routine diagnostic test to be deployed by plant pest diagnostics laboratories. Nevertheless, HTS technologies and downstream bioinformatics analysis of the generated datasets represent a complex process including many steps whose reliability must be ensured. The aim of the present guidelines is to provide recommendations for researchers and diagnosticians aiming to reliably use HTS technologies to detect plant pathogens and pests. These guidelines are generic and do not depend on the sequencing technology or platform. They cover all the adoption processes of HTS technologies from test selection to test validation as well as their routine implementation. A special emphasis is given to key elements to be considered: undertaking a risk analysis, designing sample panels for validation, using proper controls, evaluating performance criteria, confirming and interpreting results. These guidelines cover any HTS test used for the detection and identification of any plant pest (viroid, virus, bacteria, phytoplasma, mycetes, nematodes, arthropods, plants) from any type of matrix. Overall, their adoption by diagnosticians and researchers should greatly improve the reliability of pathogens and pest diagnostics and foster the use of HTS technologies in plant health., Scientific publication adapted from the deliverable 2.2 of the Valitest project - www.valitest.eu

High-throughput sequencing (HTS) is a powerful tool that enables the simultaneous detection and potential identification of any organisms present in a sample. The growing interest in the application of HTS technologies for routine diagnostics in plant health laboratories is triggering the development of guidelines on how to prepare laboratories for performing HTS testing. This paper describes general and technical recommendations to guide laboratories through the complex process of preparing a laboratory for HTS tests within existing quality assurance systems. From nucleic acid extractions to data analysis and interpretation, all of the steps are covered to ensure reliable and reproducible results. These guidelines are relevant for the detection and identification of any plant pest (e.g. arthropods, bacteria, fungi, nematodes, invasive plants or weeds, protozoa, viroids, viruses), and from any type of matrix (e.g. pure microbial culture, plant tissue, soil, water), regardless of the HTS technology (e.g. amplicon sequencing, shotgun sequencing) and of the application (e.g. surveillance programme, phytosanitary certification, quarantine, import control). These guidelines are written in general terms to facilitate the adoption of HTS technologies in plant pest routine diagnostics and enable broader application in all plant health fields, including research. A glossary of relevant terms is provided among the Supplementary Material., This article is based upon work from the work package 2 of the project VALITEST (https://www.valitest.eu/), supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 773139.

The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

The CO2 Human Emission (CHE) project is an European initiative bringing together a consortium of 22 European partners to build a prototype global CO2 source inversion system that can provide policy-relevant information on the spatiotemporal characteristics of anthropogenic CO2 emissions. This prototype shall evolve toward a new Copernicus CO2 service, which will provide a Monitoring and Verification Support (MVS) capacity that can address the challenge of the global stocktake (GST) devised under the Paris Agreement. The global inversion system will build on existing operational infrastructures (CAMS, C3S) at the European Centre for Medium-range Weather Forecast (ECMWF) to exploit ground-based measurements as well as space-based observations from current and future satellite missions (e.g., Sentinel 5p and CO2M). We will present ongoing efforts at ECMWF to develop a source inversion capability in the current operational Integrated Forecasting System (IFS), which will serve as the basis for the future global CO2 inversion prototype. Preliminary results will be discussed, that include model transport error estimations based on Monte-Carlo ensemble simulations as well as the first chemical source optimization experiments performed with the IFS 4D-Var system.

The European Union’s Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition. It uses the ECMWF Integrated Forecasting System (IFS), which includes meteorological and atmospheric composition variables, such as reactive gases, greenhouse gases and aerosols, for its global forecasts and reanalyses. The current green-house gases operational suite monitors CH4 and CO2 and assimilates TANSO and IASI retrievals for both species. The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) satellite launched in October 2017 yields a wealth of atmospheric composition data, including CH4 retrievals at unprecedented high horizontal resolution (7km) and up to daily revisit time. We used the IFS to perform monitoring experiments at different horizontal resolutions (25 km and 9 km). We also performed first data assimilation experiments at 25 km horizontal resolution.This first set of monitoring experiments shows the potential of the TROPOMI CH4 retrievals to correct known biases that exist in the current CAMS analyses and forecasts. Assimilation experiments of TROPOMI CH4 shows that adding the instrument in the operational chain would significantly improve the analysis and forecasts. Detection of CH4 sources seen by TROPOMI compared to CAMS also shows the potential of the instrument to inform on and infer anthropogenic and natural sources. For example, discrepancies between TROPOMI retrievals and CAMS fields in the CH4 levels associated with oil and gas extraction activities show very promising perspectives for monitoring and analysis of CH4 concentration and emissions. We will finally discuss the challenges and progress made towards performing inversions using the IFS operational system.

Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses as well as two higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and 9 km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16 km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of − 19.2 (16 km), − 16.7 (9 km), and − 20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul ( d CO / d CO 2 = 9 ppbv ppmv −1 ) compared to the West Sea ( d CO / d CO 2 = 28 ppbv ppmv −1 ). This “combustion signature contrast” is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8–12 ppbv ppmv −1 , the West Sea: ∼30 ppbv ppmv −1 ) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64–0.90, the West Sea: ∼0.80 ) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients ( ∼0.4 ppmv hPa −1 for CO2 and 3.5 ppbv hPa −1 for CO) in the morning samples than observed ( ∼0.25 ppmv hPa −1 for CO2 and 1.7 ppbv hPa −1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts.

Indonesia experienced an exceptional number of fires in 2015 as a result of droughts related to the recent El Nino event and human activities. These fires released large amounts of carbon dioxide (CO2) into the atmosphere. Emission databases such as the Global Fire Assimilation System version 1.2 and the Global Fire Emission Database version 4s estimated the CO2 emission to be approximately 1100 MtCO2 in the time period from July to November 2015. This emission was indirectly estimated by using parameters like burned area, fire radiative power, and emission factors. In the study presented in this paper, we estimate the Indonesian fire CO2 emission by using the column-averaged dry air mole fraction of CO2, XCO2, derived from measurements of the Orbiting Carbon Observatory-2 satellite mission. The estimated CO2 emission is 748 ± 209 MtCO2, which is about 30% lower than provided by the emission databases.

This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00 UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

El presente libro recoge los desarrollos mas relevantes a nivel mundial, las experiencias de Corpoica (hoy agrosavia) y el trabajo de decadas de los coautores nacionales e internacionales que hacen parte del mismo. La documentacion sobre los avances y las perspectivas en la materia tiene la intencion de acelerar los nuevos desarrollos en aspectos aun no estudiados del control biologico y estimular el progreso en su implementacion. Esta parte comprende informacion relacionada con la Implementacion del control biologico, ademas de reflexiones y recomendaciones de los autores sobre el futuro del control biologico.

The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones., JRC.H.7-Climate Risk Management

Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses plus two higher resolution forecasts (16-km and 9-km horizontal resolution), to assess their capability in predicting combustion signatures over East Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16-km and 9-km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the16-km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of -19.2 (16-km), -16.7 (9-km), and -20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS show a remarkable agreement with observed enhancement ratios of CO with CO2 over Seoul metropolitan and over the West Sea, where East Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (delta(CO)/delta(CO2) = 9 ppbv/ppmv) compared to the West Sea (delta(CO)/delta(CO2) = 28 ppbv/ppmv). This ‘combustion signature contrast’ is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8-12 ppbv/ppmv, West Sea: ~30 ppbv/ppmv) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64-0.90, West Sea: ~0.80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients (~0.4 ppmv/hPa for CO2 and 3.5 ppbv/hPa for CO) in the morning samples than observed (~0.25 ppmv/hPa for CO2 and 1.7 ppbv/hPa for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9-km vs 16-km) generally produce better forecasts.

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this paper. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving a greater attention than 5 to 10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and to other components of the Earth system as well as the overall computational efficiency of representing model uncertainty.

Data assimilation is a sophisticated technique, yet not available in combustion, that combines measurements to model simulation and account for uncertainties in order to improve the numerical prediction of a system. In the context of gas turbines, data assimilation may be used for example to improve the prediction of flame ignition and propagation by a smart analysis of images and measurements. A first illustration of data assimilation is given in a simple case, where synthetic time-evolving positions of the flame front are assimilated to calibrate parameters of a premixed flame model. Its successful application in the context of natural fire propagation assesses the predictive capacity of the technique and the resulting higher fidelity in the data-driven simulations.

Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a-priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a-priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 ppb and 34 ppb, respectively). In comparison, the a-priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

In this article we study the influence of different characteristics of our assimilation system on the surface ozone analyses over Europe. Emphasis is placed on the evaluation of the background error covariance matrix (BECM). Data assimilation systems require a BECM in order to obtain an optimal representation of the physical state. A posteriori diagnostics are an efficient way to check the consistency of the used BECM. In this study we derived a diagnostic to estimate the BECM. On the other hand an increasingly used approach to obtain such a covariance matrix is to estimate it from an ensemble of perturbed assimilation experiments. We applied this method, combined with variational assimilation, while analysing the surface ozone distribution over Europe. We first show that the resulting covariance matrix is strongly time (hourly and seasonally) and space dependent. We then built several configurations of the background error covariance matrix with none, one or two of its components derived from the ensemble estimation. We used each of these configurations to produce surface ozone analyses. All the analyses are compared between themselves and compared to assimilated data or data from independent validation stations. The configurations are very well correlated with the validation stations, but with varying regional and seasonal characteristics. The largest correlation is obtained with the experiments using time and space dependent correlation of the background errors. Results show that our assimilation process is efficient in bringing the model assimilations closer to the observations than the direct simulation, but we cannot conclude which BECM configuration is the best. The impact of the background error covariances configuration on four-days forecasts is also studied. Although mostly positive, the impact depends on the season and lasts longer during the winter season.

An ensemble method combined with a four-dimensional variational data assimilation system is used to provide monthly estimates of the background error covariance matrix for global stratospheric and upper tropospheric ozone for the year 2008. The system is composed of the Mocage chemical transport model and the Valentina assimilation system. The ensemble was obtained from parallel analyses of perturbed data from the Microwave Limb Sounder (MLS) instrument. The monthly estimates of background error covariances have then been introduced in the assimilation suite. To assess the separate contribution of each of its components, a number of analyses were realized, using only some estimated components of the background error covariances and a basic model for the others. The evaluation is realized by comparing the analyses with independent ozone profiles (from ozonesondes) and total ozone columns (from the Ozone Monitoring Instrument). It demonstrates that using the estimated statistics compared to basic models for the background error covariance matrix globally slightly improves the analysis quality; however, using the estimated statistics more largely improves the analysis quality for special situations encountered in April and October. In these situations, the most important parameter for the analysis quality is the use of estimated correlations. Copyright © 2011 Royal Meteorological Society

This paper presents observing system simulation experiments (OSSEs) to compare the relative capabilities of two geostationary thermal infrared (TIR) instruments to measure ozone (O3) and carbon monoxide (CO) for monitoring air quality (AQ) over Europe. The primary motivation of this study is to use OSSEs to assess how these infrared instruments can constrain different errors affecting AQ hindcasts and forecasts (emissions, meteorology, initial condition and the 3 parameters together). The first instrument (GEO-TIR) has a configuration optimized to monitor O3 and CO in the lowermost troposphere (LmT; defined to be the atmosphere between the surface and 3 km), and the second instrument (GEO-TIR2) is designed to monitor temperature and humidity. Both instruments measure radiances in the same spectral TIR band. Results show that GEO-TIR could have a significant impact (GEO-TIR is closer to the reference atmosphere than GEO-TIR2) on the analyses of O3 and CO LmT column. The information added by the measurements for both instruments is mainly over the Mediterranean Basin and some impact can be found over the Atlantic Ocean and Northern Europe. The impact of GEO-TIR is mainly above 1 km for O3 and CO but can also improve the surface analyses for CO. The analyses of GEO-TIR2 show low impact for O3 LmT column but a significant impact (although still lower than for GEO-TIR) for CO above 1 km. The results of this study indicate the beneficial impact from an infrared instrument (GEO-TIR) with a capability for monitoring O3 and CO concentrations in the LmT, and quantify the value of this information for constraining AQ models.