Operational Forecasting System Research Articles

RainForests: A Machine Learning Approach to Calibrating NWP Precipitation Forecasts

Abstract Probabilistic forecasts derived from ensemble prediction systems (EPSs) have become the standard basis for many products and services produced by modern operational forecasting centers. However, statistical postprocessing is generally required to ensure forecasts have the desired properties expected for probability-based outputs. Precipitation, a core component of any operational forecast, is particularly challenging to calibrate due to its discontinuous nature and the extreme skew in rainfall amounts. A skillful forecasting system must maintain accuracy for low-to-moderate precipitation amounts, but preserve resolvability for high-to-extreme rainfall amounts, which, though rare, are important to forecast accurately in the interest of public safety. Existing statistical and machine learning approaches to rainfall calibration address this problem, but each has drawbacks in design, training approaches, and/or performance. We describe RainForests, a machine learning approach for calibrating ensemble rainfall forecasts using gradient-boosted decision trees. The model is based on the ecPoint system recently developed at ECMWF by Hewson and Pillosu, but uses machine learning models in place of the semisubjective decision trees of ecPoint, along with some other improvements to the model structure. We evaluate RainForests on the Australian domain against some simple benchmarks and show that it outperforms standard calibration approaches both in overall skill and in accurately forecasting high rainfall conditions, while being computationally efficient enough to be used in an operational forecasting system. Significance Statement Weather forecasting typically uses physical models to simulate the atmosphere and produce a set of scenarios, called an ensemble, for each forecast time. Forecast calibration is a set of statistical techniques for using these ensemble forecasts to obtain probability forecasts of weather variables. For example, on a given day, we may predict that there are a 90% chance of at least 1 mm of rain and a 50% chance of at least 3 mm. We introduce RainForests, a new machine learning method for calibrating ensemble forecasts, which uses ensemble forecasts of rainfall and other related variables to produce more accurate probability forecasts of rainfall amounts in operational forecasting settings.

Verification of Operational Forecast Models in Cases of Extratropical Transition of North Atlantic Hurricanes

Abstract Operational forecast models are necessary for the prediction of weather events in real time. Verification of these models must be performed to assess model skills and areas in need of improvement, particularly with different types of weather events that may occur. Despite the devastating impacts that can be caused by tropical cyclones (TCs) that undergo extratropical transition (ET) and become post-tropical cyclones (PTCs), these storms have not been extensively studied in the context of short-term weather prediction. This study completes the first analysis of the Global Forecast System (GFS) and a preoperational version of the newly operational Hurricane Analysis and Forecast System (HAFS) models in forecasting the occurrence of ET and the rainfall associated with ET storms in the North Atlantic basin. GFS’s skill exceeds that of HAFS in forecasting the occurrence of ET, but HAFS tends to have lower track and rain-rate errors in the fully tropical phase of ET storms’ life cycles. Both models simulate rain rates that are often too high near the storm center and fail to capture the larger area of moderate rain rates that greatly contributes to total rainfall accumulation. The discrepancies in rain rates between the models and Integrated Multi-satellitE Retrievals for GPM (IMERG) could be attributed to the models’ tendency to keep storms too intense and too compact with an overly strong warm core, even throughout the ET process.

Predictability of the 2020 Strong Vortex in the Antarctic Stratosphere and the Role of Ozone

AbstractThe Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability.

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

How inaccurate offshore wind observations impact the quality of operational numerical weather prediction

AbstractAt Norwegian offshore platforms, wind speed is observed at heights between approx. 35 and approx. 150 m a.s.l. These observations are used to estimate 10‐m wind speed, by a power law with a constant wind shear coefficient. The resulting 10‐m wind speed is then distributed to the Global Telecommunication System (GTS) and used for assimilation and verification purposes by the meteorological community. However, multiple methods with varying results exist for the reduction of wind speed from the original measurement height to 10 m. The resulting estimate of 10‐m wind speed is therefore sensitive to the choice of method. The use of such observations may therefore be suboptimal in forecast analysis and make the forecast verification difficult to interpret. This study investigates how the use of these observations impacts forecasts from the operational regional forecast system used at MET Norway and the Global Integrated Forecast System operated by ECMWF for the period 2017–2023. Both forecast systems assimilated the observations over major parts of the period and the presented results indicate that the use of these observations increased the initial errors in the forecasts. This is confirmed in a data denial experiment over a shorter period of time. The verification results show that both forecast systems underestimate the wind speed at the original measurement heights, while the results for 10‐m verification vary with the interpolation method between original sensor height and 10 m. The forecasts show in general more wind than the GTS‐distributed estimates and are in better agreement with more advanced interpolation methods. The verification results show a strong dependency on the vertical stability of the atmosphere. The major differences between the forecast systems are that the regional system shows less bias, while the standard deviations of the errors are slightly lower in the global system.

Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.

Atmospheric Precursors of Skillful SST Prediction in the Northeast Pacific

Abstract Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability. Significance Statement Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.

An Empirical Model for Skillful Rogue Wave Forecasts

Abstract One of the leading goals of rogue wave research is to develop a robust rogue wave warning system to mitigate the danger they pose. One such system has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), called the freak wave warning system (FWWS), based on nonlinear wave effects. The FWWS predicts maximum expected wave envelope height as a risk parameter for forecasts. Recently, a data-driven alternative has been proposed by Häfner et al., which was distilled from a neural network using wave buoy observations. However, it has yet to be evaluated by a spectral wave model for application to operational wave forecasting. The data-driven, learned model emphasizes bandwidth-controlled linear superposition as the predominant mechanism in crest-to-trough rogue wave generation, while nonlinear effects are a secondary term. The present work evaluates the performance of the empirical model using output from an ECMWF global wave hindcast. We find that the prediction models based on bandwidth effects have the highest log likelihood scores, with the empirical model outperforming all other tested models. In contrast, the expected maximum envelope wave height from the FWWS does not predict the occurrence of rogue waves. These results indicate that the empirical model with wave model input is a skillful predictor and should be considered for operational implementation to improve rogue wave forecasting. Significance Statement Rogue waves are unexpectedly large and unpredictable waves. Encounters with rogue waves can result in damage to marine vessels and offshore infrastructures. Research is devoted to developing systems to predict the risk of rogue wave events. A novel predictive model has been shown to perform well in predicting rogue wave occurrences based on wave buoy observations. This model is a symbolic expression distilled from an artificial neural network that incorporates known rogue wave dynamics and that can be evaluated alongside traditional risk estimates with minor adjustments to operational forecasting systems. This study evaluates the newly proposed empirical model in a forecasting setting. Our work demonstrates the efficacy of the empirical model for operational forecasting purposes.

Development of an Advance Research WRF-Based Operational Forecast System for Forcing Ocean Models and Evaluation of Its Winds Using Buoys in the Indian Ocean

Abstract This paper presents the development of a high-resolution mesoscale atmospheric numerical model Advanced Research version of WRF (ARW 3.7, henceforth ARW)-based operational forecast system and evaluation of its surface wind forecasts using buoys and satellite data in the Indian Ocean. This was set up as part of the ocean-state forecasting system to force the operational ocean models at the Indian National Centre for Ocean Information Services (INCOIS). Evaluation of winds is carried out by comparing the ARW forecasts with ocean buoys and scatterometer observations during 2016. The analysis is conducted separately with coastal and open ocean buoys, revealing marginally better performance in the open ocean simulations. The comparison of ARW forecasted winds against offshore buoy winds shows mean differences for wind speed and direction of −0.1 m s−1 and −3.3°, with root-mean-square errors (RMSEs) of 2.1 m s−1 and 39.0° and correlations of 0.75 and 0.42, respectively. At coastal regimes, the mean differences for wind speed and direction are 0.6 m s−1 and 2.6°, with RMSEs of 2.2 m s−1 and 57° and correlations of 0.63 and 0.60, respectively. The ARW model performs reasonably well compared to the National Centre for Medium-Range Weather Forecasting (NCMRWF) model in capturing wind speed variability in the Arabian Sea compared to the Bay of Bengal. Particularly during the Very Severe Cyclonic Storm (VSCS) Vardah, the ARW model provided more accurate forecasts (high skill score 80%–90%) compared to the NCMRWF model. These results indicate the efficacy of the WRF-based forecast system in predicting surface wind fields in the Indian Ocean region, particularly in the coastal areas, thus endorsing its use for an effective operational ocean forecasting at INCOIS.

Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA

Abstract We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System (UFS) at the National Oceanic and Atmospheric Administration (NOAA), part of the US National Weather Service (NWS) operational forecasting suite. The operational implementation of the atmosphere-wave coupled Global Ensemble Forecast System version 12 (GEFSv12) in September 2020 was a critical step in NOAA’s transition to the broader community-based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations notably enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This successful endeavor encountered challenges that were addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a multidisciplinary team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts, and ongoing experimental enhancements, which altogether represent a substantial contribution to NOAA’s transition to the fully-coupled UFS framework.

Common Community Physics Package: Fostering Collaborative Development in Physical Parameterizations and Suites

Abstract The Common Community Physics Package (CCPP) is a state-of-the-art infrastructure designed to facilitate community-wide development of atmospheric physics parameterizations, support their interoperability among different modeling centers, and enable the transition of research to operations in NWP and climate modeling. The CCPP consists of two elements: the Physics (a repository of parameterizations) and the Framework (an infrastructure for interfacing the parameterizations with host models). The CCPP is a community resource: Its latest release has 23 primary parameterizations, which can be organized into six supported suites. It is distributed with a single-column model to facilitate physics development and experimentation. The Developmental Testbed Center provides support to users and developers. A key aspect of the CCPP is its interoperability, that is, its ability to be used by multiple host models. This enables synergistic collaboration among groups dispersed over various institutions and working on various models. In this article, we provide an overview of the CCPP and how it is being used in two leading modeling systems. The CCPP is part of the Unified Forecast System (UFS), is included in the NOAA operational Hurricane Analysis and Forecast System (HAFS) version 1, and is slated for use in all upcoming NOAA global and limited-area UFS applications for operations. Similarly, the CCPP has been integrated into the Navy Environmental Prediction System Using a Nonhydrostatic Engine (NEPTUNE) model and is undergoing testing for the upcoming transition to operations. These experiences make physics interoperability a reality and open the doors for much broader collaborative efforts on ESM development.

Towards unified land data assimilation at ECMWF: Soil and snow temperature analysis in the SEKF

AbstractWeather centres use a variety of data assimilation schemes to analyze different land variables in their operational forecast systems. Current activities at the European Centre for Medium‐Range Weather Forecasts (ECMWF) are working towards a unified and more consistent land data assimilation system to provide more accurate initial conditions for the atmospheric forecasts. The first step is to replace the current 1D optimal interpolation (1D‐OI) used for first‐layer soil and snow temperature analyses, and integrate multi‐layer soil and first‐layer snow temperature into the ensemble‐based simplified extended Kalman filter (SEKF) currently used for multi‐layer soil moisture. This work focuses on the technical developments and the evaluation of the atmospheric forecast skill of a series of numerical weather prediction experiments to compare different SEKF configurations with the former 1D‐OI over a three‐month summer and winter period. Using the SEKF leads to seasonally varying significant improvements in the 2‐m temperature forecast in the verification against own analyses and to slightly improved results in the validation using independent synoptic observations. This work lays the foundation for integrating additional land variables into the SEKF and investigating stronger land–atmosphere coupling.

Sensitivity of Easterly QBO’s Boreal Winter Teleconnections and Surface Impacts to SSWs

Abstract The quasi-biennial oscillation (QBO) is thought to influence boreal winter surface conditions over Asia and around the North Atlantic. Confirming if these responses are robust is complicated by the QBO having multiple pathways to influence surface conditions as well as internal variability. The reanalysis record suggests that sudden stratospheric warmings (SSWs), breakdowns of the polar vortex that can elicit persistent surface impacts, are more frequent during easterly QBO (EQBO). Hence, this modulated frequency of SSWs may account for some of the EQBO surface responses. However, many climate models do not reproduce this QBO–SSW relationship, perhaps because it is noise or because the model QBOs are deficient. We circumvent these issues by using an ensemble of fixed boundary condition branched simulations in which a realistic EQBO is prescribed in control simulations previously devoid of a QBO, allowing us to isolate the transient atmospheric response to EQBO. Imposing EQBO accelerates the tropical upper-tropospheric wind, shifts the subtropical jet poleward, and attenuates the polar vortex. Interestingly, the latter is not entirely dependent on the statistically significant increase in SSW frequency due to EQBO. Corroborating observations, EQBO is associated with warmer surface temperatures over Asia and negative North Atlantic Oscillation (NAO) conditions. We then subsample the branched/control simulations based on which EQBO members have SSWs. The negative NAO response is primarily associated with more frequent SSWs, while the Asia warming develops irrespective of SSWs. These results have implications for wintertime predictability and clarify the pairing of particular QBO teleconnections with certain surface impacts. Significance Statement The QBO is one of the few parts of the Earth system that is predictable months in advance and that also elicits global effects on surface temperature, circulation, and precipitation. Unfortunately, climate models and operational forecast systems do not simulate the QBO well and it is not always clear how robust the global impacts of the QBO are. Here, we impose the QBO in idealized model simulations, which modulates wintertime surface temperature and precipitation over Asia, the North Atlantic, Europe, and Africa in a manner consistent with observations. This work substantiates the importance of climate and forecast models properly simulating the QBO.

Operational Forest-Fire Spread Forecasting Using the WRF-SFIRE Model

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km2) was validated against the satellite-based burnt area (1.07 km2) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting.

Deep Learning of a 200-Member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events

Abstract This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve the reliability and skill of PQPFs while permitting computationally efficient model fitting using a short training dataset. The methodology is applied to postprocessing of 24-h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from 1 to 6 days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) Model, named West-WRF, to produce a 200-member ensemble in near–real time (NRT) over the western United States during the boreal cool seasons to support Forecast-Informdayed Reservoir Operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System version 12 (GEFSv12), and the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPF verification metrics from a recently developed neural network postprocessing scheme. The results demonstrate that the skill of postprocessed forecasts significantly outperforms PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy-to-extreme precipitation (e.g., >75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

Sensitivity of enhanced vertical resolution in the operational Global Forecast System (GFS) T1534 on the short to medium range forecast of Indian summer monsoon

Sensitivity of enhanced vertical resolution in the operational Global Forecast System (GFS) T1534 on the short to medium range forecast of Indian summer monsoon

The operational CMEMS wind wave forecasting system of the Black Sea

ABSTRACT Accurate wind wave data is essential for ship navigation, safety at sea, and offshore energy production. Access to real-time and forecast wave data, with quality metrics, is vital for operational services. The Copernicus Marine Environment Monitoring Service (CMEMS) operational wave forecasting system of the Black Sea relies on the third-generation WAve Model (WAM). It offers a 1-day hindcast and 10-day forecast at 2.5 km spatial and 1-hour temporal resolutions. The model is driven by ECMWF IFS010 winds as well as surface currents and sea surface heights from a hydrodynamic model. The model's validation against satellite and buoy data indicates good overall performance. For two-year satellite significant wave heights, error statistics include a root mean square difference (RMSD) of 27 cm, a bias of – 16 cm, a scatter index of 0.22, and a correlation coefficient of 0.93. Wave buoy comparisons show a RMSD of 1.09 s for the TM02 period and a 30.5° standard deviation of the mean direction. The model performs well during storm events and provides reliable wave forecasts for up to four days, despite slightly underestimating the significant wave height. The driving wind fields generally perform well, but assessments reveal shortcomings along the coasts.

Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

Abstract. Biogeochemical-Argo (BGC-Argo) float profiles provide substantial information on key vertical biogeochemical dynamics and have been successfully integrated in biogeochemical models via data assimilation approaches. Although BGC-Argo assimilation results have been encouraging, data scarcity remains a limitation with respect to their effective use in operational oceanography. To address availability gaps in the BGC-Argo profiles, an observing system experiment (OSE) that combines a neural network (NN) and data assimilation (DA) was performed here. A NN was used to reconstruct nitrate profiles, starting from oxygen profiles and associated Argo variables (pressure, temperature, and salinity), while a variational data assimilation scheme (3DVarBio) was upgraded to integrate BGC-Argo and reconstructed observations in the Copernicus Mediterranean operational forecast system (MedBFM). To ensure the high quality of oxygen data, a post-deployment quality control method was developed with the aim of detecting and eventually correcting potential sensors drift. The Mediterranean OSE features three different set-ups: a control run without assimilation; a multivariate run with assimilation of BGC-Argo chlorophyll, nitrate, and oxygen; and a multivariate run that also assimilates reconstructed observations. The general improvement in the skill performance metrics demonstrated the feasibility of integrating new variables (oxygen and reconstructed nitrate). Major benefits have been observed with respect to reproducing specific biogeochemical-process-based dynamics such as the nitracline dynamics, primary production, and oxygen vertical dynamics. The assimilation of BGC-Argo nitrate corrects a generally positive bias of the model in most of the Mediterranean areas, and the addition of reconstructed profiles makes the corrections even stronger. The impact of enlarged nitrate assimilation propagates to ecosystem processes (e.g. primary production) at a basin-wide scale, demonstrating the importance of the assimilation of BGC-Argo profiles in forecasting the biogeochemical ocean state.

Evaluation of WRF model configurations for dynamic downscaling of tropical cyclones activity over the North Atlantic basin for Lagrangian moisture tracking analysis in future climate

This study assessed five well-established physics suites of the Weather Research and Forecasting (WRF) model in operational forecasting systems in the North Atlantic (NATL) basin or from previous sensitive experiments for dynamic downscaling tropical cyclone (TC) activity. We performed long-term simulations for the 2020 TC season in the NATL and compared the WRF tracks against the HURDAT2 dataset from the US National Hurricane Center. Among the tested configurations, the analysis revealed that the Kain-Fritsch, Purdue Lin, BouLac and revised MM5 schemes for cumulus, microphysics, planetary boundary layer and surface layer, respectively (hereafter WT), outperformed all four others in terms of TC frequency, track density and intensity and showed good performance in the cyclone accumulated energy and TC landfalling locations. In addition, WT well-captured the spatial distribution of accumulated TC precipitation and moisture uptake patterns, although it overestimated the precipitation maxima. Likewise, it agreed on the relative moisture contribution from fixed moisture sources (i.e., Gulf of Mexico, Caribbean Sea, tropical NATL, and western NATL) in the NATL basin. Overall, this study highlighted the high potential of using the WT physics suite in WRF for downscaling TC activity over the NATL basin, which will be useful for TC Lagrangian moisture sources analysis in future climate.

Skill assessment of seasonal forecasts of ocean variables

There is growing demand for seasonal forecast products for marine applications. The availability of consistent and sufficiently long observational records of ocean variables permits the assessment of the spatial distribution of the skill of ocean variables from seasonal forecasts. Here we use state-of-the-art temporal records of sea surface temperature (SST), sea surface height (SSH) and upper 300m ocean heat content (OHC) to quantify the distribution of skill, up to 2 seasons ahead, of two operational seasonal forecasting systems contributing to the seasonal multi-model of the Copernicus Climate Change Services (C3S). This study presents the spatial distribution of the skill of the seasonal forecast ensemble mean in terms of anomaly correlation and root mean square error and compares it to the persistence and climatological benchmarks. The comparative assessment of the skill among variables sheds light on sources/limits of predictability at seasonal time scales, as well as the nature of model errors. Beyond these standard verification metrics, we also evaluate the ability of the models to represent the observed long-term trends. Results show that long-term trends contribute to the skill of seasonal forecasts. Although the forecasts capture the long-term trends in general, some regional aspects remain challenging. Part of these errors can be attributed to specific aspects of the ocean initialization, but others, such as the overestimation of the warming in the Eastern Pacific are also influenced by model error. Skill gains can be obtained by improving the trend representation in future forecasting systems. In the meantime, a forecast calibration procedure that corrects the linear trends can produce substantial skill gains. The results show that calibrated seasonal forecasts beat both the climatological and persistence benchmark almost at every location for all initial dates and lead times. Results demonstrate the value of the seasonal forecasts for marine applications and highlight the importance of representing the decadal variability and trends in ocean heat content and sea level.