The NASA TROPICS (Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 8 and May 25, 2023 (two CubeSats in each of the two launches into 550-km orbits with approximately 33-degree inclination). Over the course of the mission, TROPICS provided more spaceborne microwave soundings than any operational program, and the combined forecast impact was larger and more spatially coherent than that of any individual passive microwave platform, illustrating the benefit of constellation-based temporal sampling for constraining rapidly evolving tropical convection. Prior to the deorbit of the last TROPICS spacecraft in December 2025, observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution were used to conduct high-value science investigations of tropical cyclones. TROPICS has provided rapid-refresh microwave measurements (median refresh rate of better than 60 minutes early in the mission with four functional CubeSats) in twelve channels spanning 92 to 205 GHz over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Thousands of high-resolution images of tropical cyclones have been captured by the TROPICS mission, revealing detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) has provided new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, has informed tropical cyclone analysis at operational centers. The suite of TROPICS products is publicly available with much improved median revisit rates and were provided with data latencies that are sufficient to enable their use in operational tropical cyclone forecasting applications. In this presentation, we highlight the use of these high-revisit thermodynamic data from TROPICS to better characterize storm structure and environmental conditions over a variety of cases over the 30-month mission lifetime.

How to cite: Blackwell, W. and the TROPICS Science Team: A Summary of the New Capabilities for Observing Tropical Cyclone Thermodynamic Structure Provided by the NASA TROPICS Mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12774, https://doi.org/10.5194/egusphere-egu26-12774, 2026.

A multiradar mosaic is a key solution to the insufficient detection range of a single weather radar. In the traditional grid-preprocessed mosaicking method (GPM), radar polar coordinate data are interpolated into Cartesian grids to compensate for vertically undersampled regions in radar volume scans. However, such interpolation fails to accurately reconstruct the polarization parameters in these regions. Therefore, this study presentss a polar coordinate direct-mosaicking method (PDM) for the high-density radar network in South China, which directly operates on polar coordinate data and avoids initial interpolation. Based on typical precipitation cases from May to August 2021, three key issues in the PDM are addressed: First, horizontal reflectivity (Z_H) biases and differential reflectivity (Z_DR) offsets are corrected; second, the number of radars in the mosaicking process is evaluated, with five radars determined to be optimal; and third, the weights of different radar data are optimized by considering vertical and horizontal distances, along with the melting layer position. Compared with the GPM, the PDM yields a more accurate representation of the melting layer, with a smaller mean height error (192 m compared with 470 m) and a more realistic estimation of thickness (661 m compared with 1507 m). It also improves the continuity of polarimetric parameters within convective core regions. The case studies indicate that the PDM enables earlier identification of Z_DR columns and more accurate estimation of their heights. These advancements provide high-quality observational constraints for cloud microphysical research and offer potential for improving convective-scale data assimilation.

How to cite: Li, Z., Wu, C., Liu, L., and Zhang, Y.: Enhancing Convective-scale Polarimetric Signatures through a Polar Coordinate Direct-Mosaicking Method for High-density Radar Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2320, https://doi.org/10.5194/egusphere-egu26-2320, 2026.

How to cite: Chen, H. and Zhao, K.: Assimilation of Dual-Polarization Radar Data Based on Hydrometeor Classification for Improving the Short-Term Prediction of Convective Storms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17383, https://doi.org/10.5194/egusphere-egu26-17383, 2026.

How to cite: Nemani, R. S., Bannister, R. N., Lawless, A. S., Wei, H., and Thomas, C.: Machine Learning-Driven Background Error Covariances for High-Resolution Data Assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1685, https://doi.org/10.5194/egusphere-egu26-1685, 2026.

Assimilation of column-integrated observations such as precipitable water vapor (PWV) remains challenging when vertical moisture profile constraints (e.g., radiosondes) are unavailable. Ensemble data assimilation systems typically employ Gaspari–Cohn (GC) localization (e.g., in DART) to limit the spatial influence of observations and reduce spurious correlations arising from finite ensemble size. However, GC assumes homogeneous and isotropic correlations and does not represent physically driven vertical inhomogeneity, such as transient moisture structures associated with convection or moisture transport. Consequently, vertically displaced but dynamically sensitive layers may be underrepresented during PWV assimilation. The generalized Gaspari–Cohn (GenGC) localization function introduced by Gilpin et al. (2023) relaxes these assumptions by allowing localization parameters to vary spatially, enabling inhomogeneous and anisotropic correlation structures. This flexibility is particularly relevant for PWV assimilation, where the vertical distribution of moisture sensitivity can vary substantially with atmospheric state.

Sensitivity of water vapor mixing ratio (qvapor) to PWV was analyzed for Tucson, Flagstaff, Albuquerque, and Santa Teresa at 12 UTC during July–September 2021. These sensitivities were used to estimate the appropriate vertical influence of PWV assimilation and to construct a vertically varying GenGC localization. The performance of GenGC was evaluated relative to a standard GC localization with a fixed vertical radius of 3.5 km. These four locations in the Southwest US are chosen since they are impacted by the North American monsoon. To date, forecasting the monsoon precipitation is still challenging even with convective-permitting models coupled with data assimilation (Risanto et al. 2026 – in review).

Global Positioning System PWV (GPS-PWV) observations from Tucson and Flagstaff for three summer days in 2021 were assimilated into a convective-permitting (1.8 km) WRF ensemble with 40 members. HRRRv4 provided initial and boundary conditions. PWV was assimilated at 12 UTC using both GC and GenGC vertical localization, and radiosonde observations are used for independent verification.

Initial results indicate that qvapor exhibits strongest sensitivity to PWV from the surface up to approximately 6 km MSL, motivating a GenGC localization that extends vertical influence on this level. PWV analyses produced using GenGC were generally closer to observations than those using GC, reflecting the inclusion of moisture adjustments above 3.5 km MSL. In some cases, GenGC also improved near-surface qvapor relative to GC; however, larger positive qvapor biases were found above ~5 km MSL. Further investigation is required to assess the robustness of these results.

Future work will expand the number of cases and implement GenGC directly within the DART framework to evaluate its broader applicability for assimilating PWV and related datasets.

How to cite: Risanto, C. B., Gilpin, S., and Arellano, A.: Investigating the Application of Generalized Gaspari-Cohn Correlation Function in Vertical Localization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2699, https://doi.org/10.5194/egusphere-egu26-2699, 2026.

Bayesian model calibration with data assimilation methods receives continued interest for climate models, where turbulence-resolving large eddy simulations (LES) often serve as the ground truth for cloud processes. This study extends the calibration hierarchy towards the LES themselves, which are in turn validated against measurement data. Ensemble data assimilation for turbulent simulations is approached from a smoother perspective by calibrating a semi-idealistic LES simulation against averaged measurements with an ensemble Kalman smoother (EnKS).

This work re-visits the well-known test case simulating marine stratocumulus clouds (DYCOMS-II), which has been used extensively for forward model validation. Sub-grid scale (SGS) turbulence parameters are calibrated alongside the parameterized initial condition and forcing, aiming at a wholistic uncertainty representation. For the PyCLES model (dx=35 m), the calibrated setup achieves an improvement over the default model configuration used in previous studies. Experiments with different advection schemes reveal how the calibration result varies for implicit and explicit SGS modeling. For some of the tested schemes, consistent model errors on some observations require manual specification of larger data uncertainties in order to stabilize the calibration.

Through the analysis of partial increments, the EnKS methodology provides insights to parametric model sensitivities, and a means to explore a large parameter space. However, the performance is hindered by weak nonlinearities in the parameter-to-data map, which includes a nonlinear normalizing parameter transform. The practical application of EnKS-based calibration for LES models is facilitated by relying directly on a perturbed physics ensemble, which is commonly used for sensitivity studies. The results also invite to re-interpret biases found in previous model intercomparison studies on the DYCOMS-II case, as well as to consider the influence of uncertainties in initial condition and forcing when assessing parametric sensitivities in LES simulations.

How to cite: Grund, D., Mishra, S., and Schemm, S.: Bayesian Calibration of a Large-Eddy Resolving Model towards Campaign Measurements with an Ensemble Kalman Smoother, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5745, https://doi.org/10.5194/egusphere-egu26-5745, 2026.

Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation (DA) in numerical weather prediction (NWP). In this study, we investigate the applicability of conformal prediction (CP), a recent machine learning (ML) method, to quantify uncertainty in a controlled, idealized setting. We use a toy model, designed to mimic convective process. The CP provides a set of possible outcomes with a chosen confidence level. Here, we compare and evaluate the average empirical coverage, the average interval length and average interval score loss (AISL) for the three variants of CP i.e., a) Standard CP, b) Normalized CP and c) Conformalized Quantile Regression. We also combine DA with the CP estimates of uncertainty and quantify the significance of improvement. Our results highlight the strengths and limitations of each approach, providing insights into the effectiveness of CP to complement ensemble-based uncertainty quantification in simplified atmospheric models.

How to cite: George, C., Janjic, T., Javanmardi, A., and Hüllermeier, E.: Uncertainty quantification via conformal prediction in data assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7371, https://doi.org/10.5194/egusphere-egu26-7371, 2026.

An accurate forecast of T2m in complex terrain is essential for a wide range of economic and societal applications. However, improper assimilation of T2m observations can degrade forecast quality. 
The 3DVar scheme relies on a climatological error covariance matrix, which can produce unrealistic analysis increments and consequently inaccurate forecasts, particularly over sloped terrain. For example, an observation from a valley station may still generate increments at the mountaintop, even though the valley observation does not adequately represent the atmospheric conditions at the mountaintop. In contrast, 3DEnVar utilizes flow-dependent error covariances that are representative of the recent atmospheric flow regime. Hybrid-3DEnVar, on the other hand, employs an error covariance with 50\% weight assigned to the climatological covariance matrix and 50\% to the flow-dependent covariance matrix. Hybrid-3DEnVar has been tested in AROME-Austria and improved the initial conditions of AROME-Austria compared to 3DVar.  
Our study compares the T2m forecast of  Hybrid-3DEnVar against the operational 3DVar scheme of AROME-Austria in complex terrain.
For this purpose, we utilize the convective-scale limited-area ensemble forecast system (C-LAEF) Alpe Adria based on the numerical weather prediction model AROME, which operates at a horizontal grid space of 1 km. The 32-member ensemble of C-LAEF Alpe Adria (with a 1 km horizontal resolution) provides the flow-dependent error covariances. 
We conduct three sets of data denial experiments in which we run a 3-hourly assimilation cycle over 10 days, once with all observations and once with the T2m measurements removed. The first set of experiments uses 3DVar, the second 3DEnVar, and the third Hybrid-3DEnVar. We present first-guess departure statistics and forecast verification for T2m over the Alpine terrain.

How to cite: Jyoti, K., Griewank, P., Meier, F., and Weissmann, M.: Can AROME-Austria Hybrid-3DEnVar improve the forecast of 2-meter temperature (T2m) in complex Alpine terrain?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9782, https://doi.org/10.5194/egusphere-egu26-9782, 2026.

Radar data assimilation (DA) is critical for convective-scale forecasting, as it provides real-time, high-resolution information on precipitation, wind, and convective system dynamics that is not captured by surface observations or satellite data. Polarimetric radar observations complement conventional reflectivity (Zh) and radial velocity (Vr) measurements by enabling the determination of hydrometeor types and particle size characteristics. Differential reflectivity (ZDR) is one of the key polarimetric radar variables, defined as the ratio between horizontal and vertical reflectivity, that provides direct information on hydrometeor shape and size. Despite its strong potential to better constrain storm microphysics and improve convective-scale forecasts, the assimilation of ZDR remains challenging. Challenges associated with observation operators, error characterisation, and data quality underscore the need for further research in this area.

The current study investigates the direct assimilation of ZDR in an observing system simulation experiment (OSSE) of a long-lived supercell. The OSSE is conducted using the ICOsahedral Nonhydrostatic (ICON) model with a two-moment microphysics scheme and the Local Ensemble Transform Kalman Filter (LETKF), employing both hydrometeor mixing ratios and number concentrations as analysis variables. Synthetic observations are generated using the polarimetric radar forward operator EMVORADO developed at the Deutscher Wetterdienst. The synthetic ZDR observations are assimilated in addition to the non-polarimetric variables, namely Zh and Vr, while a reference experiment assimilating only non-polarimetric synthetic observations was conducted for comparison. A series of sensitivity experiments are performed to assess the impact of DA settings on assimilation performance, for varying observation error, localisation radius, and ensemble size. In addition, appropriate thresholds and no-reflectivity (clear air) equivalents for ZDR observations are examined.

How to cite: Bardachova, T., Janjic, T., Carrassi, A., de Lozar, A., and Mendrok, J.: Direct assimilation of differential reflectivity in an idealised setup , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10222, https://doi.org/10.5194/egusphere-egu26-10222, 2026.

Partial analysis increments (PAI) are an efficient diagnostic to determine the influence individual observations or groups of observations on the analysis (Diefenbach et al.). Addittionally, the diagnostic can be used to approximate the influence that observations would have had for different assimilation settings. We use PAI to investigate whether observations could be more beneficial if they were assimilated using different localization settings. Localization is an essential component of any ensemble-based data-assimilation system, necessary to mitigate the effects of a limited ensemble size and to reduce the computational cost. Localization for satellite observations, which lack a constant or well-defined observation location, is particularly challenging, and numerous approaches have been proposed. Using PAI, we can estimate the performance of many different localization approaches without needing to rerun experiments and determine settings that lead to an optimal analsis using independent observations for verification. In this poster, we present results from a one-month-long cycled forecast of the regional modelling system of Deutscher Wetterdienst, in which the PAIs are compared against non-assimilated radiosondes. PAI is used to optimise and understand localisation by (1) considering a range of localisation functions over a normalised metric; (2) studying different combinations of parameters for a Gaspari-Cohn localization function and (3) optimising localization over a set of idealised functions. The current settings of DWD for vertical localisation of satellite radiances in the visible 0.6µm and infrared 6.2µm 7.3µm channels - which were originally designed to improve estimates of cloud-related variables - perform well against our metrics but could be improved upon by using a multi-peaked localisation function.

How to cite: Griewank, P., Diefenback, T., Necker, T., Parker, M., Schomburg, A., and Weissmann, M.: Optimizing localization for ensemble data assimilation using partial analysis increments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13080, https://doi.org/10.5194/egusphere-egu26-13080, 2026.

The atmospheric coherence length is a key parameter for quantitatively describing the strength of optical turbulence and is crucial for laser propagation, astronomical observation, and turbulence-degraded image restoration. Although existing studies have widely used convolutional neural networks (CNNs) to retrieve this parameter from single-frame distorted images, they fail to fully utilize the dynamic characteristics of turbulence. To address this, this study proposes a CNN-based inversion model incorporating dynamic optical flow features. By integrating the optical flow method with CNNs, the model captures and utilizes the turbulent motion information between consecutive image frames. The input to the model is the optical flow displacement field calculated from two successive image frames, and the angle-of-arrival fluctuation variance derived from the optical flow is incorporated into the loss function as a physical constraint. This design significantly enhances sensitivity to subtle image distortions induced by weak turbulence, effectively overcoming the performance bottleneck of traditional static single-frame input models under weak turbulence conditions.

The model was trained on a simulated dataset and validated using measured data obtained on June 13-14, 2022, at Science Island in Hefei, Anhui Province, China. The measured data were collected synchronously by an atmospheric coherence length monitor and an imaging device over a 500-meter horizontal near-ground propagation path. The study systematically compared the performance of four classic CNN architectures (AlexNet, VGGNet, EfficientNet, CVTStegoNet) with and without the incorporation of TV-L1 optical flow features. The results show that the introduction of optical flow features universally and significantly improves the inversion accuracy and robustness of the models under different turbulence strengths. Specifically, the method achieved faster training convergence and superior generalization performance across all tested models. On a test set comprising 4,500 training samples and 500 independent validation samples, the model with integrated optical flow features reduced the root mean square error (RMSE) and mean relative error (MRE) by an average of approximately 40%, while the coefficient of determination (R²) was generally above 0.99. Among the four models, the fused model based on AlexNet achieved the best overall performance.

This work demonstrates the critical role of utilizing turbulent dynamic features in enhancing the accuracy of inversion models, offering a novel and practical deep learning solution for high-precision, real-time detection of the atmospheric coherence length.

How to cite: Wang, Y., Wang, P., Huang, Y., and Mei, H.: Inversion of Atmospheric Coherence Length Using Optical Flow-based Convolutional Neural Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16922, https://doi.org/10.5194/egusphere-egu26-16922, 2026.