Regional Data Assimilation Research Articles

Numerical Simulation of Storm Waves during Typhoon Maysak and Haishen in the Korean Peninsula

In this study, we simulated storm waves induced by typhoon Maysak and Haishen in the Korean peninsula by using the third-generation numerical wave model SWAN(Simulating WAves Nearshore). We used the sea-surface wind data of KMA(Korea Meteorological Agency)’s operational meteorological model RDAPS(Regional Data Assimilation Prediction System), and JMA(Japan Meteorological Agency)’s weather forecast model JMA-MSM(Meso-Scale Model) as input forcing of the numerical wave model. In addition, the wind field of the tropical cyclone was established with JTWC(Joint Typhoon Warning Center)’s best track data for the storm wave simulation. We used two different source term options, which consider other physics of energy generation by wind and dissipation by whitecapping. Validation of the simulation result was conducted over statistical analysis with the observation data from KMA and KHOA(Korea Hydrographic and Oceanographic Agency)’s buoys. As a result, the whole scenario tended to overestimate significant wave height. The simulation result by using RDAPS as input forcing outperformed the result by using JMA-MSM. It denoted that the performance of a wave model depends on the accuracy of input forcing wind. The wind field by JTWC’s best track data was not suitable compared with the meteorological numerical model. ST6 simulated the wave height higher than Komen’s source term. However, Komen's source term simulated storm waves better than ST6 in this study, and further researches under various conditions are required to evaluate ST6 for the storm waves simulation. In the simulation of wave period parameters, it was hard to estimate the accuracy of the model, and other approaches are necessary for the evaluation.

A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign

This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November–December 2018). This system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF). It combines multiple data sources both global and locally available like high-resolution surface networks, AMDAR data from local aircraft flights, soundings, AIRS retrievals, high-resolution GOES-16 wind estimates, and local radar data. Hourly analyses with grid spacing of 10 km are generated along with warm-start 36-h ensemble-forecasts, which are initialized from the rapid refresh analyses every three hours. A preliminary evaluation shows that a forecast error reduction is achieved due to the assimilated observations. However, cold-start forecasts initialized from the Global Forecasting System Analysis slightly outperform the ones initialized from the regional assimilation system discussed in this paper. The system uses a multi-physics approach, focused on the use of different cumulus and planetary boundary layer schemes allowing us to conduct an evaluation of different model configurations over central Argentina. We found that the best combinations for forecasting surface variables differ from the best ones for forecasting precipitation, and that differences among the schemes tend to dominate the forecast ensemble spread for variables like precipitation. Lessons learned from this experimental system are part of the legacy of the RELAMPAGO field campaign for the development of advanced operational data assimilation systems in South America.

Optimization of SWAN Wave Model to Improve the Accuracy of Winter Storm Wave Prediction in the East Sea

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen’s formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA’s) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts’ newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA’s) meso-scale forecasting data. We analyzed the accuracy of each model’s results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

The Spatial Maps of Paddy Rice Yield over Northeast Asia Using COMS Geostationary Satellite and Reanalysis Meteorological Data

This study estimated rice yield maps for Northeast Asia by using the Communication, Ocean and Meteorological satellite (COMS), Terra satellite, and Regional Data Assimilation and Prediction System (RDAPS) of the numerical model. The rice yield is highly useful in the study for crop information monitoring according to climate change as well as agriculture information, industry, and economy. This study produced rice yield maps for Northeast Asia including Korea, North Korea, Japan, and three northeastern provinces of China (Heilongjiang, Jilin, and Liaoning) from 2011 to 2017. The estimated spatial resolution of the rice yield maps in Northeast Asia is 500 m. The spatial observation range is 25 ° N ~ 47 ° N and 115 ° E ~ 145 ° E. In order to estimate rice yield, Remote Sensing-integrated Crop Model was employed in this study. The inputs of the RSCM are vegetation indices from Geostationary Ocean Color Imager (GOCI) of the COMS, solar radiation from Meteorological Imager of the COMS, Land Surface Water Index from the MODerate Resolution Imaging Spectroradiometer, and the temperature from the RDAPS were considered as input data. In particular, this study applied the Bidirectional Reflectance Distribution Function to the GOCI time-series images to calculate more improved vegetation indices by minimizing the directional error generated in the satellite observation location. These indices were very effective in the simulation of the rice yield.

Relative impact of observations on a regional Arctic numerical weather prediction system

AbstractThe impact of Arctic conventional and satellite observations on regional short‐range weather forecasts is assessed using observing‐system experiments, in which observations are removed (denied) when creating the initial conditions of the forecasts. The experiments are conducted with the AROME‐Arctic regional mesoscale numerical weather prediction system, using as lateral boundary conditions (LBCs) observing‐system experiments performed at the European Centre for Medium‐Range Forecasts (ECMWF) with the global forecasting system. This allows the assessment of the relative impacts of observations on forecast skill through regional data assimilation (DA), through LBCs, and the total impact due to the denial of observations in both the regional and global forecasting systems. The study is conducted during the first and second Special Observing Periods of the Year Of Polar Prediction. The total impact on the upper‐air forecasts is dominated by the impact of observations through their assimilation in the LBCs, while for the winter period the impact on surface fields is dominated by the regional DA. The latter is significant up to 36 hr, while the former impact can last throughout the verified forecast range (48 hr). The use of observations in the LBCs has both significantly positive and significantly negative impacts. In terms of total impact on forecast skill, conventional observations, followed by infrared radiances, have the largest impact on all upper‐air parameters, except for humidity. For upper‐air humidity forecasts, the microwave radiances have the largest impact. In terms of observation impact through regional DA, conventional observations play the largest role for upper‐air temperature and geopotential, microwave for upper‐air humidity, and atmospheric motion vectors and Infrared Atmospheric Sounding Interferometer (IASI) for wind forecast. Regional DA of conventional observations also contributes the most to improvements of surface fields, except for 10‐m winds, for which the microwave temperature‐sensitive radiances are the most important.

Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data.

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

NO x Emission Changes Over China During the COVID-19 Epidemic Inferred From Surface NO2 Observations.

The COVID‐19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NOx emissions are inferred using a regional data assimilation system and hourly surface NO2 measurement over China. The results show that because of the coronavirus outbreak, NOx emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NOx emissions fell by more than 60% and ~30% in many large cities and most small to medium cities, respectively. Overall, NOx emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NOx emissions in China.

Sub-daily soil moisture estimate using dynamic Bayesian model averaging

Accurate estimation of soil moisture (SM) from satellite products and model simulations at sub-daily timescale remains a challenge. This study proposes a general dynamic Bayesian model averaging (BMA) framework for merging sub-daily model products. Compared to the traditional BMA method, this study introduces adaptive weights (dynamically variant with time) for BMA members. Based on the previous evaluation work, a subset of model products is selected from eight model products as BMA members. The dynamic BMA experiment is performed for the surface SM (0–10 cm) model products at sub-daily (6-h) timescale in 2017 over the Yangtze-Huaihe river basin. The results are compared with the automatic SM observations (ASMOs) with unprecedented high spatial and temporal resolution (up to 7 stations within a 104 km2 pixel; hourly). Because weather pattern and model performance change over time, the determination of an optimal training period is critical to obtain adaptive BMA weights for rapid weather regime changes. The sensitivity of training length (days) is then examined, and the optimum data length used in the BMA training period proves to be about 80 days. With deterministic and probabilistic verification metrics, the dynamic BMA estimated SM is comprehensively evaluated against the ASMOs, eight global model products, and the CMA’s (China Meteorological Administration) regional Land Data Assimilation System (CLDAS) product. To better compare the probability distribution of different products, the cumulative distribution function (CDF) consistency histogram and a more objective metric consistency deviation (CD) are proposed to diagnose the consistency of two SM CDFs (e.g., the BMA estimated and the observed CDF). In terms of both the deterministic (the Kling-Gupta efficiency, correlation, system bias, and bias adjusted root-mean square error) and probabilistic verification methods (CD, QQ-plots, and reliability), the dynamic BMA estimated SM outperforms any BMA members and even the benchmark product CLDAS. This study demonstrates that the dynamic BMA framework provides a new solution for merging SM model products. The merged SM and the BMA combined probability distribution can be further used for drought monitoring and prediction.

Efficient Regional Hybrid Ensemble-Variational Data Assimilation using the Global-Ensemble-Model-Augmented Error Covariance for Numerical Weather Prediction over Eastern China

An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as the complementary low-resolution regional ensemble error covariance. The high-resolution dynamic ensemble mean is used as the first guess in hybrid EnVar and then re-centered to the updated high-resolution dynamic ensemble perturbations after minimization analysis. In this study, the proposed method is implemented into the Weather Research and Forecasting Model’s (WRF) data assimilation system coupled with the ensemble transform Kalman filter (ETKF) and preliminarily tested for numerical weather prediction during the Mei-Yu season over eastern China. It is found that the experiment containing fewer regional dynamic ensemble members but augmented with global ensemble error covariance obtains similar results to the experiment containing many more regional dynamic ensemble members. However, the former experiment only takes up one third of the latter experiment’s computational cost. The method proposed in this study also outperforms the 3DVar, hybrid EnVar using the pure global ensemble error covariance, as well as the hybrid EnVar using regional ETKF ensemble with a smaller size. The method proposed in this paper effectively combines the contributions of the ensemble error covariance from both the global and the regional models to produce better initial conditions for the regional WRF data assimilation system.

SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore

AbstractSINGV‐DA is a convective‐scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV‐DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV‐DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system.

An improved forecast of precipitation type using correlation-based feature selection and multinomial logistic regression

Accurate prediction of precipitation type is an important part of weather forecasting. But using meteorological insight to make such predictions from a small set of weather variables achieves only limited success. We use correlation-based feature selection to assemble an effective subset of the large number of weather variables available in short-range weather forecasts, and from these we obtain the coefficients for multinomial regression, which can then be used to predict precipitation type. We applied this approach to data for significant locations in South Korea, obtained from the European Centre for Medium-Range Weather Forecasts and from the Regional Data Assimilation and Prediction System, and achieved predictions which are respectively 15% and 13% more accurate than those contained in the original forecasts. • We improved the forecast of winter precipitation types in South Korea. • Feature selection was used to assemble an effective subset of weather variables. • A comparative analysis was conducted on various forecasting schemes. • Logistic regression with feature selection works well on precipitation forecast.

Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods

Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.

Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation

A high-resolution ocean circulation model for the Indian Ocean (IO) using Regional Ocean Modeling System (ROMS) is operational at Indian National Centre for Ocean Information Services (INCOIS) which provides ocean state forecasts for the Bay of Bengal (BoB) and the Arabian Sea (AS) to the Indian Ocean rim countries. To provide an improved estimate of ocean state, a variant of Ensemble Kalman Filter (EnKF), viz., the Local Ensemble Transform Kalman Filter (LETKF) has been developed and interfaced with the present basin-wide operational ROMS. This system assimilates in-situ temperature and salinity profiles and satellite track data of sea-surface temperature (SST). The ensemble members of the assimilation system are initialized with different parameters like diffusion and viscosity coefficients and are subjected to an ensemble of atmospheric fluxes. In addition, one half of the ensemble members respond to K profile parameterization mixing scheme while the other half is subjected to Mellor–Yamada mixing scheme. This strategy aids in arresting the filter divergence which has always been a challenging task. The assimilated system simulates the ocean state better than the present operational ROMS. Improvements permeate to deeper ocean depths with better correlation and reduced root-mean-squared deviation (RMSD) with respect to observations particularly in the northern Indian Ocean which is data rich in density. Analysis shows domain averaged RMSD reduction of about 0.2–0.4 °C in sea surface temperature and 2–4 cm in sea level anomaly. The assimilated system also manages to significantly improve the thickness of the temperature inversion layers and the duration of its occurrence in northern Bay of Bengal. The most profound improvements are seen in currents, with an error reduction of 15 cm/s in zonal currents of central Bay of Bengal.

Application of Cumulative Distribution Functions to Compositing Precipitable Water with Low Earth Orbit Satellite Data

Precipitable water (PW) is a leading indicator of severe weather, such as heavy rainfall. The National Meteorological Satellite Center receives observation data directly from Low Earth Orbit (LEO) satellites, including NOAA-18, 19, MetOp-A, B, and Suomi-NPP, and generates level 2 products in near real time for the East Asia region. In this study, PW data were retrieved from the abovementioned sources and composited to determine meteorologically significant PW fields from all available data sources considering the boundary layer (BL), middle layer (ML), high layer (HL), and total column. Cumulative distribution function (CDF) matching was performed for PW retrieved from the five satellites within specific time windows in order to integrate them and minimize their differences. The PW composite data were matched to reference PW CDF data from the Unified Model (UM) Regional Data Assimilation Prediction System (RDAPS) analysis data, which are updated according to selected time windows. PW data were composited for 03:00–09:00, 09:00–15:00, 15:00–21:00, and 21:00–03:00 UTC time windows. PW data composited during the summer and winter seasons in 2015, 2016, and 2017 were then validated. The results show improvements in composite Total Precipitable Water and BL PW from Suomi-NPP of approximately 15% and 40% (RMSE), respectively.

Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS) were established in 2004, significant progress has been made in development of regional and global LDASs. National, regional, project-based, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications (from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure, parameters, and forcing are discussed.

Regional Data Assimilation with the NCMRWF Unified Model (NCUM): Impact of Doppler Weather Radar Radial Wind

The Indian sub-continent often receives heavy rainfall events, especially during active phases of the south-west monsoon (SWM). The accurate prediction of such events requires a high-resolution model with data assimilation techniques which can assimilate high-density spatial and temporal observed data, like radar observations. This study is undertaken to evaluate the impact of assimilation of radial velocity from multiple Doppler weather radars (DWRs) for simulation of three convective rainfall events during the SWM using the high-resolution (4 km) National Centre for Medium Range Weather and Forecasting (NCMRWF) Unified Model (NCUM) with a three-dimensional variational (3DVAR) data assimilation system. Two numerical experiments are carried out viz. control simulation (CNTL) and a second experiment (RAD, assimilation of all observations used in CNTL plus radial winds from Indian DWRs). The study clearly shows that the assimilation of DWR data has a positive impact in the simulation of heavy rainfall events by the model. The root mean square error (RMSE) of the analyzed (simulated) u- and v-components of wind is reduced by 46% (25%) and 37% (19%) in the RAD experiment as compared to the CNTL experiment. The simulated wind fields at different pressure levels are stronger after assimilation of DWR observations, and it is also successfully produced the cyclonic circulation as compared to the CNTL simulation. The skew-T plots suggested that the convection is more properly represented by the RAD experiment than the CNTL. The value of stability parameters is properly simulated in the RAD experiment, while compared with the CNTL, the values are reasonably well matched with the observed values in all cases. The intensity of the rainfall is reasonably well simulated by the RAD as compared to the CNTL experiment. The statistical skill scores of rainfall with different thresholds are significantly improved in the RAD experiment for all the cases.

A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations-a case study of Xuzhou, China.

Carbon monoxide (CO) emission inventory data are crucial for air quality control. However, the emission inventories are labor-intensive and time-consuming and generally have large uncertainties. In this study, we developed a new regional data assimilation system (TracersTracker) for estimating the surface CO emission flux from continuous mixing ratio observations using the proper orthogonal decomposition (POD)-based four-dimensional variational (4D-VAR) data assimilation method (POD-4DVar) and a coupled regional model (Weather Research and Forecasting model (WRF) with the Models-3 Community Multi-scale Air Quality (CMAQ) model). This system was applied to estimate CO emissions in Xuzhou city, China. An experiment was conducted with the continuous hourly surface CO mixing ratio observations from 21 monitoring towers in January and July of 2016. The experimental results of the system were examined and compared with the continuous surface CO observations (a priori emission). We found that the retrieved CO emission fluxes were higher than the a priori emission and were mainly distributed in urban and industrial areas, which were 104% higher in January (winter) and 44% higher in July (summer).

Assimilation of MWHS and MWTS radiance data from the FY-3A satellite with the POD-3DEnVar method for forecasting heavy rainfall

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun-3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.

Contribution of future wide-swath altimetry missions to ocean analysis and forecasting

Abstract. The impact of forthcoming wide-swath altimetry missions on the ocean analysis and forecasting system was investigated by means of OSSEs (observing system simulation experiments). These experiments were performed with a regional data assimilation system, implemented in the Iberian–Biscay–Ireland (IBI) region, at 1∕12∘ resolution using simulated observations derived from a fully eddy-resolving free simulation at 1∕36∘ resolution over the same region. The objective of the experiments was to assess the ability of different satellite constellations to constrain the ocean analyses and forecasts, considering both along-track altimeters and future wide-swath missions; consequently, the capability of the data assimilation techniques used in the Mercator Ocean operational system to effectively combine the different kinds of measurements was also investigated. These assessments were carried out as part of a European Space Agency (ESA) study on the potential role of wide-swath altimetry in future versions of the European Union Copernicus programme. The impact of future wide-swath altimetry data is evident for investigating the reliability of sea level values in OSSEs. The most significant results were obtained when looking at the sensitivity of the system to wide-swath instrumental error: considering a constellation of three nadir and two “accurate” (small instrumental error) wide-swath altimeters, the error in ocean analysis was reduced by up to 50 % compared to conventional altimeters. Investigating the impact of the repetitivity of the future measurements, the results showed that two wide-swath missions had a major impact on sea-level forecasting – increasing the accuracy over the entire time window of the 5-day forecasts – compared with a single wide-swath instrument. A spectral analysis underlined that the contributions of wide-swath altimetry data observed in ocean analyses and forecast statistics were mainly due to the more accurate resolution, compared with along-track data, of ocean variability at spatial scales smaller than 100 km. Considering the ocean currents, the results confirmed that the information provided by wide-swath measurements at the surface is propagated down the water column and has a considerable impact (30 %) on ocean currents (up to a depth of 300 m), compared with the present constellation of altimeters. The ocean analysis and forecasting systems used here are those currently used by the Copernicus Marine Environment and Monitoring Service (CMEMS) to provide operational services and ocean reanalysis. The results obtained in the OSSEs considering along-track altimeters were consistent with those derived from real data (observing system experiments, OSEs). OSSEs can also be used to assess the potential of new observing systems, and in this study the results showed that future constellations of altimeters will have a major impact on constraining the CMEMS ocean analysis and forecasting systems and their applications.

CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model–data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC yr−1. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91±1.10 PgC yr−1) and CarbonTracker Europe (version CTE2016, -0.91±0.31 PgC yr−1). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.