Observing System Simulation Experiments Research Articles

Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.

An Investigation on Joint Data Assimilation of a Radar Network and Ground-Based Profiling Platforms for Forecasting Convective Storms

Abstract A summer convective precipitation case, occurring in eastern China on 16–17 July 2020, is selected to investigate the impact of joint assimilation of ground-based profiling platforms and weather radars on forecasting convective storms using observational system simulation experiments (OSSEs). The simulated profiling platforms include the Doppler wind lidar (DWL), a wind profiler (WP), and a microwave radiometer (MWR). Results show that joint assimilation of WP and radar data produces a better analysis of convective dynamical structure than joint assimilation of DWL and radar data, since WP detects deeper layer winds. Joint assimilation of MWR and radar data enables rapid adjustment of temperature and humidity and thus, avoids the potential errors introduced by the latent heat term of the radar diabatic initialization in the early stage. Profiling observations in a horizontal spacing of 80 km provide fewer benefits for convective forecasting, while reducing the spacing to 40 km can dramatically improve model analysis and forecasts. Joint assimilation of multiple profiling observations in a 20-km horizontal spacing with radar data exhibits a beneficial synergistic effect and mitigates “the ramp-down issue” during the forecast stage. Assimilating profiling observations with an update interval less than 30 min does not have as pronounced an effect on convective forecasts as horizontal spacing. Furthermore, assimilating profiling observations at a 20-km horizontal spacing can obtain accurate mesoscale background environment and forecast storms with an ability comparable to radar data assimilation. This work emphasizes the need to consider implementing a joint mesoscale detection system that incorporates weather radars and profiling observations for leveraging convective storm forecasting.

Impacts of Marine Surface Pressure Observations from a Spaceborne Differential Absorption Radar Investigated with an Observing System Simulation Experiment

Abstract A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

A Region‐Optional Targeted Observation Method and Its Application in the Sea Surface Temperature Prediction Associated With the Indian Ocean Dipole

AbstractAlthough data assimilation‐based targeted observation approaches have been widely used, obtaining optimal observation sites over a specific region for a prediction target remains a challenge. Hence, this study developed a more practical region‐optional targeted observation method by introducing a projection vector, allowing for the prediction targeted region to be different from the observation one. By minimizing the analysis error variance in a targeted region, the method identified optimal sites through a sequential assimilation framework. This region‐optional method was applied in the targeted observation study of the sea surface temperature (SST) prediction associated with Indian Ocean Dipole (IOD). The first 10 optimal observation sites were identified, with seven sites in the west IO, and three in the east. The results were further validated by conducting observation system simulation experiments using an ensemble adjustment Kalman filter assimilation system in the Community Earth System Model (CESM). The assimilation of observations from the 10 optimal sites was capable of reducing root mean squared errors (RMSEs) by 38.2% when assessing the SST across the IOD key regions, significantly more than the reduction from 10 optimal sites identified via the conventional method, or that from 10 random sites. This improvement was primarily due to the error reduction in the eastern IO, where SST RMSEs were reduced by >50%. The proposed region‐optional targeted observation method can seek optimal sites in any region of interest and is not confined to the targeted region as in conventional algorithms, thus providing a more reasonable method for designing optimal observation networks.

Evaluation of new radio occultation observations among small satellites at Venus by data assimilation

We conducted observing system simulation experiments (OSSEs) for radio occultation measurements (RO) among small satellites, which are expected to be useful for future Venus missions. The effectiveness of the observations based on realistic orbit calculations was evaluated by reproduction of the “cold collar”, a unique thermal structure in the polar atmosphere of Venus. Pseudo-temperature observations for the OSSEs were provided from the Venus atmospheric GCM in which the cold collar was reproduced by the thermal forcing. The vertical temperature distributions between 40 and 90 km altitudes at observation points were assimilated. The result showed that the cold collar was most clearly reproduced in the case where the temperature field in high-latitudes was observed twice a day, suggesting that the proposed observation is quite effective to improve the polar atmospheric structure at least. Although the cold collar was also reproduced in the OSSEs for Longwave Infrared Camera (LIR) observations, the result seemed unrealistic and inefficient compared to that obtained in the RO OSSEs. The present study shows that the OSSEs can be used to evaluate observation plans and instruments in terms of reproducibility of specific atmospheric phenomena, and applied to future missions targeting planetary atmospheres.

Assimilating AMSU-A Radiance Observations with an Ensemble Four-Dimensional Variational (En4DVar) Hybrid Data Assimilation System

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localization in implementation, but more difficult to directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations of radiance observations are undetermined and the transmission of observational information is thereby obstructed. To determine the vertical coordinates of radiance observations, a weighted average hypsometry is proposed. Using this hypsometry, AMSU-A radiance observations are directly assimilated with an ensemble four-dimensional variational (En4DVar) DA system. It consists of a four-dimensional ensemble-variational (4DEnVar) system providing ensemble covariance and a 4DVar system. Observing system simulation experiments show that the hypsometry alleviates the degradations in the late period of medium-range forecast in the Northern Extratropics that occur in the traditional peak-based hypsometry. It obviously improves the analysis qualities and forecast skills of the En4DVar system and its two components, especially in the Southern Extratropics, when incorporating AMSU-A radiance observations. The improvement in the En4DVar-initialized forecast is comparable to that in the 4DVar-initialized forecast in the Southern Extratropics and Tropics. It indicates that a proper hypsometry enables efficient extraction of useful information from AMSU-A radiance observations by 4DEnVar with observation space localization. Therefore, the 4DEnVar provides high-quality ensemble covariances for En4DVar.

Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis

Accurate reporting of point source emissions of CO2 is fundamental to addressing climate change. Currently, bottom-up verification methods based on inventory statistics face significant challenges in this area. Satellite remote sensing has emerged as a promising approach for cost-effective global-scale verification of point source emissions, with spaceborne LiDAR offering high spatial resolution ideal for this purpose. However, the inversion of CO2 emissions from spaceborne LiDAR CO2 concentration observations requires urgent attention, as existing methods heavily rely on prior information in diffusion models and the accuracy of meteorological data. In this work, a novel emission inversion method based on genetic algorithms and trust-region techniques is proposed to estimate CO2 emissions from point sources using spaceborne LiDAR observations. A comparison between the CO2 emission rates calculated from actual airborne LiDAR data (as a prototype of spaceborne LiDAR) and emission inventories for the Suizhong power plant showed a deviation of less than 7.0%. Observing system simulation experiment (OSSE) demonstrated that using DQ-1 (spaceborne LiDAR) observation data as input, the relative error of emission rates would be less than 0.6% when the distance between the emission source and the observation footprint is less than 10 km. Furthermore, the developed model mitigates the impact of uncertainties in meteorological data and IPDA (Integrated-Path Differential Absorption) LiDAR measurements on the final emission quantification. The proposed approach is expected to enable DQ-1 to provide affordable and accurate carbon verification services for over 20.0% of the world's strong point source emissions.

The GMAO Hybrid 4D-EnVar Observing System Simulation Experiment Framework

Abstract This work describes the extension of the Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework to use a hybrid four-dimensional ensemble–variational (4D-EnVar) scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short intercomparison of upgrading from a 3D to a 4D OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify particular features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

A XCO2 Retrieval Algorithm Coupled Spatial Correlation for the Aerosol and Carbon Detection Lidar

We describe the approach to estimating the atmospheric carbon dioxide (CO2) for the Aerosol and Carbon Detection Lidar (ACDL) onboard the Atmospheric Environment Monitoring Satellite (AEMS). The method estimates the optimal state vector by maximizing the measurement posterior probability under a given prior state vector probability distribution. A priori constraint considering the spatial correlation is used as regularization to solve the ill-posed problem. We ran a series of observing system simulation experiments to demonstrate the critical outcome and character percentage uncertainty reduction. The results show that the state vector uncertainty can be reduced by ∼ 10% near the surface for the single sounding. The CO2 column-averaged dry air mole fraction (XCO2) derived by this algorithm is more stable than that obtained by the conventional algorithm and enables the monitoring of concentration changes for the multiple soundings. Similar to the Total Carbon Column Observing Network (TCCON), the averaging kernel is also provided for the subsequent flux inversion. Our simulation experiments demonstrate that the structure of the prior error covariance plays an important role in revealing vertical information from observations. In addition, we applied this algorithm to an airborne ACDL experiment for the retrieval of atmospheric CO2 over Bohai Bay on March 14, 2019. AEMS’s observations with a small footprint will yield important information on the carbon cycle, especially for small but strong emission sources.

Control simulation experiments of extreme events with the Lorenz-96 model

Abstract. The control simulation experiment (CSE) is a recently developed approach to investigate the controllability of dynamical systems, extending the well-known observing system simulation experiment (OSSE) in meteorology. For effective control of chaotic dynamical systems, it is essential to exploit the high sensitivity to initial conditions for dragging a system away from an undesired regime by applying minimal perturbations. In this study, we design a CSE for reducing the number of extreme events in the Lorenz-96 model. The 40 variables of this model represent idealized meteorological quantities evenly distributed on a latitude circle. The reduction of occurrence of extreme events over 100-year runs of the model is discussed as a function of the parameters of the CSE: the ensemble forecast length for detecting extreme events in advance, the magnitude and localization of the perturbations, and the quality and coverage of the observations. The design of the CSE is aimed at reducing weather extremes when applied to more realistic weather prediction models.

Removal of the Vertical Structure Ambiguity in the Thermodynamic Retrieval from Multiple-Doppler Radar Synthesized Wind Fields

Abstract It has been long recognized that in the retrieved thermodynamic fields using multiple-Doppler radar synthesized winds, an unknown constant exists on each horizontal level, leading to an ambiguity in the retrieved vertical structure. In this study, the traditional thermodynamic retrieval scheme is significantly improved by the implementation of the Equation of State (EoS) as an additional constraint. With this new formulation, the ambiguity of the vertical structure can be explicitly identified and removed from the retrieved three-dimensional thermodynamic fields. The only in situ independent observations needed to perform the correction are the pressure and temperature measurements taken at a single surface station. If data from multiple surface stations are available, a strategy is proposed to obtain a better estimate of the unknown constant. Experiments in this research were conducted under the observation system simulation experiment (OSSE) framework to demonstrate the validity of the new approach. Problems and possible solutions associated with using real datasets and potential future extended applications of this new method are discussed.

SIR‐HUXt—A Particle Filter Data Assimilation Scheme for CME Time‐Elongation Profiles

AbstractWe present SIR‐HUXt, the integration of a sequential importance resampling data assimilation scheme with the HUXt solar wind model. SIR‐HUXt assimilates the time‐elongation profiles of Coronal Mass Ejection (CME) fronts in the low heliosphere, like those extracted from heliospheric imager (HI) data. Observing System Simulation Experiments are used to explore SIR‐HUXt's performance for a simple synthetic CME scenario of an Earth directed CME in a uniform solar wind, where the CME is initialized with the average CME speed and width. These experiments are performed for a range of observer locations, from 20° to 90° behind Earth, spanning the L5 point where ESA's Vigil mission will return HI data for operational space weather forecasting. For this idealized scenario, SIR‐HUXt performs well at constraining the CME speed, and has some success at constraining the CME longitude while the CME width is largely unconstrained by SIR‐HUXt. Rank‐histograms suggest the SIR‐HUXt ensembles are well calibrated, with no indications of bias or under/over dispersion. Improved constraints on the initial CME speed lead to improvements in the CME transit time and arrival speed. For an L5 observer, SIR‐HUXt reduced the transit time and arrival speed uncertainties by 69% and 63%. Therefore, SIR‐HUXt could improve the real‐world representivity of HUXt simulations and reduce the uncertainty of CME arrival time forecasts. The idealized scenario studied here likely enhances SIR‐HUXt's performance relative to the challenge of simulating real‐world CMEs and solar wind conditions. Future work should validate SIR‐HUXt with case studies of real CMEs in structured solar wind.

Demonstrating the Potential Impacts of Assimilating FY-4A Visible Radiances on Forecasts of Cloud and Precipitation with a Localized Particle Filter

Abstract The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4A (FY-4A) satellite provides visible radiances that contain critical information on clouds and precipitation. In this study, the impact of assimilating FY-4A/AGRI all-sky visible radiances on the simulation of a convective system was evaluated with an observing system simulation experiment (OSSE) using a localized particle filter (PF). The localized PF was implemented into the Data Assimilation Research Testbed (DART) coupled with the Weather Research and Forecasting (WRF) Model. The results of a 2-day data assimilation (DA) experiment generated encouraging outcome at a synoptic scale. Assimilating FY-4A/AGRI visible radiances with the localized PF significantly improved the analysis and forecast of cloud water path (CWP), cloud coverage, rain rate, and rainfall areas. In addition, some positive impacts were produced on the temperature and water vapor mixing ratio in the vicinity of cloudy regions. Sensitivity studies indicated that the best results were achieved by the localized PF configured with a localization distance that is equivalent to the model grid spacing (20 km) and with an adequately short cycling interval (30 min). However, the localized PF could not improve cloud vertical structures and cloud phases due to a lack of related information in the visible radiances. Moreover, the localized PF was compared with the ensemble adjustment Kalman filter (EAKF) and it was indicated that the localized PF outperformed EAKF even when the number of ensemble members was doubled for the latter, indicating a great potential of the localized PF in assimilating visible radiances.

Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method

A seasonal predictability barrier has long been noticed in ENSO forecasting with numerical models. Previous studies explored the impact of seasonal optimal initial perturbation evolutions in sea surface temperature anomalies (SSTA) on ENSO forecasting using the intermediate coupled model (ICM) via the conditional nonlinear optimal perturbation (CNOP) method. In this paper, we investigate the joint effects of SSTA and sea level anomalies (SLA) from the perspective of the optimal growth initial error (OGE). After determining the four seasonal OGEs about SSTA and SLA (i.e., SSTA-OGE, SLA-OGE and Joint-OGE), we first demonstrate the patterns, evolutions and the resulting spring predictability barrier (SPB) of the above OGEs. Then, we analyze the mechanism of OGE evolutions and SPB. Finally, we conduct observing system simulation experiments to determine the best (economic) observation network. Our experimental results indicate that the ENSO evolution error induced by SSTA-OGE and Joint-OGE presents season dependency, but SLA-OGE has no impact on ENSO evolution. Moreover, Joint-OGEs induce error evolutions and the SPB with more significant intensity than SSTA-OGEs and SLA-OGEs. From mechanism analyses, the evolutions of SSTA-OGEs are mainly dominated by Bjerknes feedback. Further, the evolution dynamics of Joint-OGEs primarily contain the continuous heating between the upper ocean combined with Bjerknes feedback and thermal diffusion in response to the discharge process. In addition, comprehensive and economical sensitive areas are identified through Joint-OGE, including the central-eastern equatorial Pacific and the western and north-eastern tropical Pacific boundary, which contribute to the ENSO prediction benefits reaching 58.31% on average.

Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ

We previously introduced the parametric variance Kalman filter (PvKF) assimilation as a cost-efficient system to estimate the dynamics of methane analysis concentrations. As an extension of our development, this study demonstrates the linking of PvKF to a 4D-Var inversion aiming to improve on methane emissions estimation in comparison with the traditional 4D-Var. Using the proposed assimilation–inversion framework, we revisit fundamental assumptions of the perfect and already optimal model state that is typically made in the 4D-Var inversion algorithm. In addition, the new system objectively accounts for error correlations and the evolution of analysis error variances, which are non-trivial or computationally prohibitive to maintain otherwise. We perform observing system simulation experiments (OSSEs) aiming to isolate and explore various effects of the assimilation analysis on the source inversion. The effect of the initial field of analysis, forecast of analysis error covariance, and model error is examined through modified 4D-Var cost functions, while different types of perturbations of the prior emissions are considered. Our results show that using PvKF optimal analysis instead of the model forecast to initialize the inversion improves posterior emissions estimate (~35% reduction in the normalized mean bias, NMB) across the domain. The propagation of analysis error variance using the PvKF formulation also tends to retain the effect of background correlation structures within the observation space and, thus, results in a more reliable estimate of the posterior emissions in most cases (~50% reduction in the normalized mean error, NME). Our sectoral analysis of four main emission categories indicates how the additional information of assimilation analysis enhances the constraints of each emissions sector. Lastly, we found that adding the PvKF optimal analysis field to the cost function benefits the 4D-Var inversion by reducing its computational time (~65%), while including only the error covariance in the cost function has a negligible impact on the inversion time (10–20% reduction).

4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry

Abstract. The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Water and Ocean and Topography) altimeter mission. Operational systems, however, generally fail to retrieve mesoscale dynamics for horizontal scales below 100 km and timescales below 10 d. Here, we address this challenge through the 4DVarnet framework, an end-to-end neural scheme backed on a variational data assimilation formulation. We introduce a parameterization of the 4DVarNet scheme dedicated to the space–time interpolation of satellite altimeter data. Within an observing system simulation experiment (NATL60), we demonstrate the relevance of the proposed approach, both for nadir and nadir plus SWOT altimeter configurations for two contrasting case study regions in terms of upper ocean dynamics. We report a relative improvement with respect to the operational optimal interpolation between 30 % and 60 % in terms of the reconstruction error. Interestingly, for the nadir plus SWOT altimeter configuration, we reach resolved space–timescales below 70 km and 7 d. The code is open source to enable reproducibility and future collaborative developments. Beyond its applicability to large-scale domains, we also address the uncertainty quantification issues and generalization properties of the proposed learning setting. We discuss further future research avenues and extensions to other ocean data assimilation and space oceanography challenges.

Recommendations for the design of in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission

The new Surface Water and Ocean Topography (SWOT) satellite mission aims to provide sea surface height (SSH) measurements in two dimensions along a wide-swath altimeter track with an expected effective resolution down to 15–30 km. In this context our goal is to optimize the design of in situ experiments aimed to reconstruct fine-scale ocean currents (~20 km), such as those that will be conducted to validate the first available tranche of SWOT data. A set of Observing System Simulation Experiments are developed to evaluate different sampling strategies and their impact on the reconstruction of fine-scale sea level and surface ocean velocities. The analysis focuses (i) within a swath of SWOT on the western Mediterranean Sea and (ii) within a SWOT crossover on the subpolar northwest Atlantic. From this evaluation we provide recommendations for the design of in situ experiments that share the same objective. In both regions of study distinct strategies provide reconstructions similar to the ocean truth, especially those consisting of rosette Conductivity Temperature Depth (CTD) casts down to 1000 m and separated by a range of distances between 5 and 15 km. A good compromise considering the advantages of each configuration is the reference design, consisting of CTD casts down to 1000 m and 10 km apart. Faster alternative strategies in the Mediterranean comprise: (i) CTD casts down to 500 m and separated by 10 km and (ii) an underway CTD with a horizontal spacing between profiles of 6 km and a vertical extension of 500 m. In the Atlantic, the geostrophic velocities reconstructed from strategies that only sample the upper 500 m depth have a maximum magnitude ~50% smaller than the ocean truth. A configuration not appropriate for our objective in both regions is the strategy consisting of an underway CTD sampling one profile every 2.5 km and down to 200 m. This suggests that the thermocline and halocline need to be sampled to reconstruct the geostrophic flow at the upper layer. Concerning seasonality, the reference configuration is a design that provides reconstructions similar to the ocean truth in both regions for the period evaluated in summer and also in winter in the Mediterranean.

Assessment of predictability of the Loop Current in the Gulf of Mexico from observing system experiments and observing system simulation experiments

This study presents results from numerical model experiments with a high-resolution regional forecast system to evaluate model predictability of the Loop Current (LC) system and assess the added value of different types of observations. The experiments evaluate the impact of surface versus subsurface observations as well as different combinations and spatial coverage of observations on the forecasts of the LC variability. The experiments use real observations (observing system experiments) and synthetic observations derived from a high-resolution independent simulation (observing system simulation experiments). Model predictability is assessed based on a saturated error growth model. The forecast error is computed for the sea surface height fields and the LC frontal positions derived from the forecasts and control fields using two metrics. Estimated model predictability of the LC ranges from 2 to 3 months. Predictability limit depends on activity state of the LC, with shorter predictability limit during active LC configurations. Assimilation of subsurface temperature and salinity profiles in the LC area have notable impact on the medium-range forecasts (2–3 months), whereas the impact is less prominent on shorter scales. The forecast error depends on the uncertainty of the initial state; therefore, on the accuracy of the analysis providing the initial fields. Forecasts with the smallest initial error have the best predictive skills with reliable predictability beyond 2 months suggesting that the impact of the model error is less prominent than the initial error. Hence, substantial improvements in forecasts up to 3 months can be achieved with increased accuracy of initialization.

Understanding the impact of assimilating FORMOSAT‐7/COSMIC‐2 radio occultation refractivity on tropical cyclone genesis: Observing system simulation experiments using Hurricane Gordon (2006) as a case study

AbstractStudies have shown that assimilating the radio occultation (RO) observations, including those from the FORMOSAT‐3/COSMIC (constellation observing systems for meteorology, ionosphere, and climate) (FS3‐C), provides positive impacts on tropical cyclone (TC) forecasts. The FS3‐C's successor, the FORMOSAT‐7/COSMIC‐2 (FS7‐C2), provides denser spatial data coverage over the Tropics and Subtropics, where severe weather systems often occur. This study investigates the impact of FS7‐C2 refractivity profiles on the prediction of TC genesis. A quick observing system simulation experiment is conducted for the period when Hurricanes Helene and Gordon (2006) occurred over the North Atlantic Ocean using a regional ensemble data assimilation system. Though assimilating FS3‐C or FS7‐C2 ROs successfully reproduces Helene's development, assimilating FS7‐C2 ROs better captures the genesis and development of Gordon with abundant moisture and vorticity in Gordon's core region, providing conditions favorable for the development of deep convection. A minimum area‐mean total precipitable water vapor of 54 mm, as well as the existence of mid‐level cyclonic vorticity (e.g., 500 hPa), at the storm core region in the initial condition is required for forecasting Gordon's genesis. Also, the assimilation of FS7‐C2 ROs in our experiments reduces the 500 hPa geopotential error by 22% and improves probabilistic quantitative precipitation forecast compared with assimilating FS3‐C ROs. Two sensitivity tests are conducted to evaluate the impact of low‐level negatively biased FS7‐C2 RO profiles and the removal of FS7‐C2 data below 3 km on Gordon's genesis. The former test does not degrade Gordon's genesis forecast skills due to a dipole error correlation between the background ROs and the moisture field over an observed RO profile near Gordon. The latter test does degrade Gordon's forecast skills but is still better than the assimilation of FS3‐C ROs since the features of low‐level moisture and mid‐level vorticity are preserved to some extent.

Estimation of CH4emission based on an advanced 4D-LETKF assimilation system

Abstract. Methane (CH4) is the second major greenhouse gas after carbon dioxide (CO2) which has substantially increased during recent decades in the atmosphere, raising serious sustainability and climate change issues. Here, we develop a data assimilation system for in situ and column-averaged concentrations using a local ensemble transform Kalman filter (LETKF) to estimate surface emissions of CH4. The data assimilation performance is tested and optimized based on idealized settings using observation system simulation experiments (OSSEs), where a known surface emission distribution (the truth) is retrieved from synthetic observations. We tested three covariance inflation methods to avoid covariance underestimation in the emission estimates, namely fixed multiplicative (FM), relaxation-to-prior spread (RTPS), and adaptive multiplicative. First, we assimilate the synthetic observations at every grid point at the surface level. In such a case of dense observational data, the normalized root mean square error (RMSE) in the analyses over global land regions is smaller by 10 %–15 % in the case of RTPS covariance inflation method compared to FM. We have shown that integrated estimated flux seasonal cycles over 15 regions using RTPS inflation are in reasonable agreement between true and estimated flux, with 0.04 global normalized annual mean bias. We then assimilated the column-averaged CH4 concentration by sampling the model simulations at Greenhouse Gases Observing Satellite (GOSAT) observation locations and time for another OSSE. Similar to the case of dense observational data, the RTPS covariance inflation method performs better than FM for GOSAT synthetic observation in terms of normalized RMSE (2 %–3 %) and integrated flux estimation comparison with the true flux. The annual mean averaged normalized RMSE (normalized mean bias) in LETKF CH4 flux estimation in the case of RTPS and FM covariance inflation is found to be 0.59 (0.18) and 0.61 (0.23), respectively. The χ2 test performed for GOSAT synthetic observations assimilation suggests high underestimation of background error covariance in both RTPS and FM covariance inflation methods; however, the underestimation is much higher (>100 % always) for FM compared to RTPS covariance inflation method.