AS1.12 | Developments in convective-scale and satellite data assimilation and observations
EDI
Developments in convective-scale and satellite data assimilation and observations
Convener: Tomislava Vukicevic | Co-conveners: Isaac Moradi, Tijana Janjic, Derek J. Posselt, Tobias NeckerECSECS, M. Bateni
Orals
| Wed, 26 Apr, 14:00–15:45 (CEST)
 
Room 1.85/86
Posters on site
| Attendance Wed, 26 Apr, 16:15–18:00 (CEST)
 
Hall X5
Orals |
Wed, 14:00
Wed, 16:15
Storm and convective-scale weather data analysis and prediction still present significant challenges for atmospheric sciences.
Addressing these challenges requires a synergy of advances in high-resolution observations, modeling, and data assimilation.
Especially assimilating satellite observations bears great potential for improving future storm-scale predictions.

This session invites contributions from developments in
● Convective-scale data assimilation techniques and models
● Active and passive satellite data assimilation
● Assessment of the impact of satellite and convective-scale data assimilation on prediction
● Model uncertainty representation in convective scale data assimilation
● Observations at convective scales: data products, observing strategies, observation operators, remote sensing, and new technologies
● Machine learning in convective scale data assimilation and forecasting

Orals: Wed, 26 Apr | Room 1.85/86

Chairpersons: Tijana Janjic, Isaac Moradi, Tobias Necker
14:00–14:05
14:05–14:15
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EGU23-9488
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Highlight
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On-site presentation
Jussi Leinonen, Ulrich Hamann, Ioannis Sideris, and Urs Germann

Convection is a complex spatiotemporal process, which has made it a particularly attractive application for deep learning, which excels at both spatial and temporal reasoning. We have developed deep learning models for predicting the occurrence of hazards caused by convective storms, so that this information may be used by forecasters, emergency services and infrastructure managers to respond to the threats caused by these hazards.

Our network is based on a recurrent-convolutional architecture that can process input data at multiple resolutions. It issues probabilistic predictions of hazard occurrence, currently up to 1 hour to the future. As inputs, we use data from weather radars, geostationary satellites, ground-based lightning detections, numerical weather predictions and digital elevation models. We have studied the importance of each data source to the quality of the predictions, finding that radar-based inputs contribute most to the prediction quality; however, some hazards can be well predicted also without radar, indicating that it is plausible to create warning systems for these hazards in areas where radar networks are not available.

In this presentation, we will describe the model architecture and case studies, as well as our experiences so far in bringing the model to real-time use by forecasters and automated warning systems at MeteoSwiss. We will also discuss future directions of this research.

How to cite: Leinonen, J., Hamann, U., Sideris, I., and Germann, U.: Predictive hazards from convective systems with deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9488, https://doi.org/10.5194/egusphere-egu23-9488, 2023.

14:15–14:25
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EGU23-2324
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On-site presentation
Helene Brogniez, Remy Roca, Jean-Pierre Chaboureau, Franck Auguste, Ilhem Gharbi, Thomas Lefebvre, Thomas Fiolleau, and Dominique Bouniol

Deep convection plays a fundamental role in the climate system by transporting from the lower layers of the atmosphere to the free troposphere, air, water and momentum. Although its study has been the subject of intense and rich scientific activities for decades, our ignorance of the vertical distribution of convective movements in the heart of convective cells is today an important scientific and operational obstacle. Only space-borne observations can meet the needs in documentation necessary to progress on the science of the water and energy cycle and simultaneously improve numerical forecasting systems. Pending the emergence (hypothetical) of microwave missions in geostationary orbit with high repeatability (~ 1 minute), an approach based on satellite constellations in convoys could provide a first response.

The “Convective Core Observations through MicrOwave Derivatives in the trOpics”, or C2OMODO for short, proposes to rely on 2 passive microwave radiometers with a multispectral sampling of the 183 and 325 GHz lines in a mini-train of 2 satellites. The time-spacing of 60 to 180sec between the 2 swaths encompasses information on the updraft motions of hydrometeors, and is thus used to characterize the intensity and the size of individual updrafts in deep convective systems.

We will present this original observational strategy, associated to the NASA / AOS general framework, as well as its expected added-value for the characterization of deep convection.

How to cite: Brogniez, H., Roca, R., Chaboureau, J.-P., Auguste, F., Gharbi, I., Lefebvre, T., Fiolleau, T., and Bouniol, D.: Observing the dynamics of deep convection using a tandem of spaceborne microwave radiometers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2324, https://doi.org/10.5194/egusphere-egu23-2324, 2023.

14:25–14:35
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EGU23-10494
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On-site presentation
William Blackwell

The NASA TROPICS Earth Venture (EVI-3) CubeSat constellation mission will provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS will provide rapid-refresh microwave measurements (median refresh rate of approximately 60 minutes for the baseline mission) 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. The TROPICS constellation mission comprises four 3U CubeSats (5.4 kg each) in two low-Earth orbital planes. Each CubeSat comprises a Blue Canyon Technologies bus and a high-performance radiometer payload to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. TROPICS spatial resolution, measurement sensitivity, and calibration accuracy and stability are all comparable with current state-of-the-art observing platforms. Two launches for the TROPICS constellation mission are planned for the Summer of 2023. Data will be downlinked to the ground via the KSAT-Lite ground network. NASA's Earth System Science Pathfinder (ESSP) Program Office approved the separate TROPICS Pathfinder mission, which launched on June 30, 2021, in advance of the TROPICS constellation mission as a technology demonstration and risk reduction effort. The TROPICS Pathfinder mission continues to yield excellent data over 18+ months of operation and has provided an opportunity to checkout and optimize all mission elements prior to the primary constellation mission. This presentation will describe the on-orbit results for the successful TROPICS Pathfinder precursor mission and will describe the recent development progress for the TROPICS constellation mission and discuss recent activities to improve the data latency and generation of near-real-time products for forecasting applications.

How to cite: Blackwell, W.: 18+ Months of Tropical Cyclone and Convective Storm Observations with the NASA TROPICS Pathfinder Satellite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10494, https://doi.org/10.5194/egusphere-egu23-10494, 2023.

14:35–14:45
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EGU23-10858
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On-site presentation
Ziad Haddad, Sai Prasanth, Sue van den Heever, Peter Marinescu, Sean Freeman, and Derek Posselt

Moisture convergence, latent heat, upper-level divergence all contribute to the genesis and growth of convective updrafts. In order to characterize the morphology of this evolution, and identify its constituent modes, we analyzed a large data set of synthetic updrafts simulated using a convection-resolving differential-equation solver run at high spatial and temporal resolutions (respectively 100 meters and 10 seconds). The analysis started by fitting each simulated updraft with a 6-parameter analytic representation, so that the joint statistics of the 6 parameters and of their evolution in time can be quantified. The first result is that an effective 6-parameter representation does exist and approximates the vertical profiles with a residual relative error whose r.m.s. value is smaller than 10% for 59% of all cases, and smaller than 20% for 89% of all cases. The r.m.s value of the absolute error is smaller than 0.4 m/s for 97% of all cases. Having established the suitability of this approximation, the variability of the 6 parameters for the 2-minute average Wa of a profile W was quantified, as was the variability of the evolution of W – Wa over a two-minute interval. The analysis reveals that 4 scalars suffice to capture the bulk of the variability of the evolution of convective updrafts. The modes (spanning the range of values of these 4 scalars) turn out to be related to the maximum amplitudes of w and to the heights at which they are achieved. This description paves the way toward the characterization of the environmental determinants of updraft evolution and, in turn, the determination of the effects of updraft characteristics on upper-level air density, divergence and the resulting anvil clouds.

How to cite: Haddad, Z., Prasanth, S., van den Heever, S., Marinescu, P., Freeman, S., and Posselt, D.: Parametrizing the evolution of convective updraft vertical velocities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10858, https://doi.org/10.5194/egusphere-egu23-10858, 2023.

14:45–14:55
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EGU23-9876
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ECS
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On-site presentation
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Lukas Kugler and Martin Weissmann

Clouds are the first area-wide observable signal of convection. Although heavily used in nowcasting applications, the use of cloud-affected satellite observations in data assimilation is very limited.

This work aims to estimate the potential impact of assimilating cloud-affected satellite observations of visible (0.6 µm) and near thermal infrared wavelength (6.2 µm and 7.3 µm) relative to the impact of assimilating radar reflectivity observations. The observation types are evaluated in observing system simulation experiments (OSSE) featuring two cases: isolated and scattered supercells. In the first case, a supercell is triggered by a warm bubble (temperature perturbation) with uncertain location and strength but equal evolution in time. In the second case, random perturbations give rise to numerous supercells scattered throughout the domain, which are in different stages of their lifetime. Observations are simulated using the radiative transfer model RTTOV/MFASIS and assimilated by the Ensemble Adjustment Kalman Filter in the Data Assimilation Research Testbed (DART). The Weather Research and Forecasting (WRF) model at 2-km grid resolution was used for forecasts. 

Results show that the forecast impact is notably different in the two cases. For example, the Fractions Skill Score of precipitation and cloudiness indicates that satellite observations can be as beneficial as three-dimensional radar reflectivity observations in the first case, in which the prior contains no error in the stage of storm development but only in horizontal position and strength. Hence, the vertical structure information contained in three-dimensional radar reflectivity does not seem to add value compared to satellite observations, resulting in a similar impact of both observation types. In the second case, however, three-dimensional radar observations constrain the vertical structure and improve upon forecasts that only use satellite observations.

How to cite: Kugler, L. and Weissmann, M.: Assimilating cloud-affected visible & infrared satellite observations in idealized simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9876, https://doi.org/10.5194/egusphere-egu23-9876, 2023.

14:55–15:05
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EGU23-6378
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ECS
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On-site presentation
Santos J. González-Rojí and Christoph C. Raible

Data assimilation techniques can improve the simulation of regional precipitation and temperature over complex regions. Nowadays, regional climate simulations using convection-permitting scales are becoming available, but the number of those simulations including an additional data assimilation scheme is rather small because of their high computational costs. Hence, it is important to evaluate the effect of data assimilation schemes for such convection-permitting simulations, and to determine if data assimilation produces any improvement in the simulation of temperature or precipitation fields.

To investigate this, we employ the Weather Research and Forecasting model (WRF; version 3.8.1) to dynamically downscale the state-of-the-art ERA5 reanalysis over Western Europe. A 3 km spatial resolution grid is employed, together with 51 vertical levels. The temporal resolution of the WRF outputs is one hour. Two model configurations are tested in two experiments spanning the period 2010-2020 after a one-year spin-up. In the first experiment (NoDA), after the initialization of the model, the boundary conditions drive the model. The second experiment (DA) is configured the same way as NoDA, but the additional 3DVAR data assimilation step (WRFDA) is run every six hours (00, 06, 12 and 18 UTC – analysis times). Observations obtained from the PREPBUFR dataset (NCEP ADP Global Upper Air and Surface Weather Observations) are employed, and only those included inside a 120 min window around analysis times were assimilated. For DA, monthly varying background error covariance matrices were created. In both cases, the model uses the Noah-MP land surface model, and high-resolution daily-varying SST fields from the NOAA OI SST v2 data set instead of the SST field from ERA5.

The results of this study show that both experiments produce similar monthly precipitation patterns to those from observational data sets such as IMERG and CHIRPS, or the reanalysis ERA5. However, in general, and particularly during summer months, DA produces larger amounts of precipitation than NoDA. These amounts are in line with those from CHIRPS. In terms of temperature, DA show colder temperatures than NoDA in most of the months, which again are similar to those from observational data sets such as CRU or EOBS. The monthly temperature patterns of both experiments are similar to those from both observational data sets. These results highlight the fact that NoDA already is able to generate reliable precipitation and temperature fields compared to diverse gridded observational data sets, but the 3DVAR data assimilation can additionally improve the performance of the regional model when convection-permitting scales are employed.

How to cite: González-Rojí, S. J. and Raible, C. C.: The effect of 3DVAR data assimilation and convection-permitting scales on the simulation of precipitation and temperature over Western Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6378, https://doi.org/10.5194/egusphere-egu23-6378, 2023.

15:05–15:15
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EGU23-12321
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ECS
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On-site presentation
Masashi Minamide and Derek Posselt

The development of atmospheric deep moist convection has been a challenging topic for numerical weather prediction, due to its chaotic nature of the development with multi-scale physical interactions. We recently found that greater than 20-km scale (as commonly known as meso-α (2000-200 km) and meso-β (200-20 km) scales) initial features helped to constrain the general location of convective activity with a few hours of lead time, but meso-γ (20-2 km) or even smaller scale features with less than 30-minute lead time were identified to be essential for capturing the spatiotemporal features of individual convection. To examine the potentials of ensemble-based data assimilation in capturing the individual convective development, as well as the subsequent development of severe weather events, we have conducted large ensemble convection-permitting data assimilation experiments with all-sky infrared satellite radiances from the latest-generation geostationary satellites. We found that the greater number of ensembles more effectively suppressed the spurious correlation for convective-scale data assimilation. However, the exact signals of convective development were not clearly captured in covariances even with thousands of ensemble members. These results suggest the potential limitation of the traditional “Eulerian” (i.e. physical grid-based) ensemble approach in convective-scale data assimilation.

How to cite: Minamide, M. and Posselt, D.: Predictability of moist convection through ensemble-based convective-scale data assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12321, https://doi.org/10.5194/egusphere-egu23-12321, 2023.

15:15–15:25
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EGU23-14476
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ECS
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On-site presentation
Guannan Hu and Sarah L. Dance

Convection-permitting data assimilation requires observations with high spatial density and high temporal frequency to provide information on appropriate scales for high resolution forecasting. Those observation types (e.g., geostationary satellite data) were found to exhibit strong spatial error correlations. Explicitly introducing correlated error statistics in the assimilation may increase the computational complexity and parallel communication costs of the matrix-vector multiplications with the observation precision matrices (the inverse observation error covariance matrices). Therefore, without suitable approaches we cannot take full advantage of the new observation uncertainty estimates. In this work, we present a new numerical approximation method, called the local SVD-FMM, which is developed based on a particular type of the fast multipole method (FMM) using a singular value decomposition (SVD), and a domain localization approach. The basic idea of the local SVD-FMM is to divide the observation domain into boxes of (approximately) equal size and then separates the calculations of the matrix-vector products according to the domain partition. These calculations can be done in parallel with very low communication overheads. Moreover, the local SVD-FMM is easy to implement and applicable to a wide variety of the precision matrices. We applied the local SVD-FMM in a simple variational data assimilation system and found that the computational cost of the variational minimisation was dramatically reduced while preserving the accuracy of the analysis. This new method has the potential to be used as an efficient technique for practical data assimilation applications where a large volume of observations with mutual error correlations needs to be assimilated in a short period of time.

How to cite: Hu, G. and Dance, S. L.: A Novel Numerical Approximation Method for Computations with Spatially Correlated Observation Error Statistics in Data Assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14476, https://doi.org/10.5194/egusphere-egu23-14476, 2023.

15:25–15:35
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EGU23-10561
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ECS
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On-site presentation
Rakesh Teja Konduru, Jianyu Liang, and Takemasa Miyoshi

This study investigates the impact of high frequency, such as 3-hourly and 1-hourly satellite microwave radiances, in global atmospheric data assimilation. To understand the impact of such a high-frequency satellite radiances data assimilation, we designed an observing system simulation experiment (OSSE) using the global NICAM-LETKF system at 56 km horizontal resolution. A free run was conducted with the NICAM model and treated as the reference (Nature) for the OSSE experiments. With the NICAM-LETKF system, we conducted five experiments, without data assimilation (NoDA), with only conventional data assimilation but not satellite radiances (NoSat), 6-hourly (6H), 3-hourly (3H), and 1-hourly (1H) satellite clear-sky radiances assimilation. The results showed that satellite microwave radiances assimilation improved the forecast of air temperature and wind over the global ocean compared to NoSat experiments. With the increase in the assimilation frequency of the satellite radiances, the air temperature and winds showed improvement in their representation over the ocean but degraded over land. Over the ocean, microwave radiances assimilation improved the typhoon eyewall wind intensities and its structure for 1H satellite radiances assimilation compared to 6H. These improvements in the wind intensities are prominent during the landfall stage of the typhoon. Forecasting landfall storms' strong winds are essential for disaster prevention and mitigation.

How to cite: Konduru, R. T., Liang, J., and Miyoshi, T.: High-frequency microwave satellite radiances data assimilation using NICAM-LETKF in the OSSE framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10561, https://doi.org/10.5194/egusphere-egu23-10561, 2023.

15:35–15:45
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EGU23-10418
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ECS
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On-site presentation
Chih-Chun (Gina) Chou, Paul J. Kushner, and Stéphane Laroche

The European Space Agency (ESA)’s Aeolus mission, launched in August 2018, provides the first global horizontal line-of-sigh (HLOS) wind profile measurements. Many Numerical Weather Prediction (NWP) centres, including ECMWF, DWD, Météo-France, Met-Office and ECCC, have shown that assimilating Aeolus winds improves overall forecast skill, especially in the tropics and data-sparse regions. To better characterize the locations and drivers of improved skill from Aeolus, we use a series of Observing System Experiments (OSEs) with the ECCC Global Deterministic Prediction System (GDPS) covering the period July to September 2019 and December 2019 to March 2020. Three experiments are used: CNTRL, CNTRL+Aeolus, and CNTRL-winds. All the observations assimilated in the GDPS are included in the CNTRL experiment. The Aeolus winds are added in the CNTRL+Aeolus experiment and the operational wind observations are withheld in the CNTRL-wind experiment. The impact of the operational winds and Aeolus are quantified by comparing the forecast error of the CNTRL-winds and CNTRL experiments with the CNTRL and CNTRL+Aeolus experiments. 

As expected, the operational winds improve the tropospheric forecast over the tropics the most, with a normalized forecast error of 8% for the wind field. By adding the Aeolus winds, which account for less than 1% of the observations, the tropospheric forecast further improves by 0.7-0.9% over the tropics and the Arctic, and by 0.5-0.6% over the data-sparse Southern Hemisphere extra-tropics. The added value of Aeolus winds is further highlighted when its impact on forecasts as a function of length scale is investigated, using a spherical harmonic decomposition. The impact is measured as the difference of the 250-hPa kinetic energy forecast error spectra between experiments. The impact of operational winds and Aeolus is dominated by the transient component whose impact is nearly four times greater than the impact on the mean component. The operational winds largely improve the forecast of global scale to intermediate scale in the short-range forecasts. The impact then decreases as forecast range increases. On the other hand, the impact of Aeolus is mostly seen in the intermediate to large scale range with a peak around spherical harmonics of degree 9 (scales about 4000 km), and is the smallest on day 1 and increases until days 4 to 5. This analysis suggests that Aeolus winds provide estimates of the wind state that are valuable and complementary to that provided from current operational winds.

How to cite: Chou, C.-C. (., Kushner, P. J., and Laroche, S.: Assessing the added value of Aeolus winds in the ECCC forecast system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10418, https://doi.org/10.5194/egusphere-egu23-10418, 2023.

Posters on site: Wed, 26 Apr, 16:15–18:00 | Hall X5

Chairpersons: Tobias Necker, M. Bateni
X5.22
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EGU23-17558
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ECS
Hristo Georgiev Chipilski and Derek Posselt

Recent work has demonstrated that convective-scale model parameters, such as those related to cloud microphysical schemes, are nonlinearly related to dynamic/thermodynamic variables in forecasts and observations. This leads to errors when data assimilation (DA) schemes based on linear-Gaussian assumptions are used to estimate the uncertain model parameters. Nonlinear modifications to the standard ensemble Kalman filter (EnKF) have been shown to perform better for systems governed by convective dynamics, and recent algorithms leveraging advances in AI/ML appear to be especially promising.

 

In this talk, we will present results from previous experiments that demonstrate how and why linear EnKF methods fall short for the challenging task of nonlinear parameter estimation. We will discuss the potential improvements that may result from a new class of ensemble DA algorithms leveraging the powerful framework of latent Gaussian models. In particular, two generalizations of the classical EnKF will be described – one which exploits the special mathematical properties of invertible neural networks (ECTF) and another one based on ideas from measure transport in the context of two-step ensemble filtering (TGA-EnKF). The advantages of these new methods will be illustrated through idealized DA experiments, which will then motivate further discussion on their applicability to convective-scale DA problems.

How to cite: Chipilski, H. G. and Posselt, D.: Toward the Application of Nonlinear Ensemble Data Assimilation Methods to Convective-Scale Parameter Estimation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17558, https://doi.org/10.5194/egusphere-egu23-17558, 2023.

X5.23
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EGU23-8429
Derek Posselt

Deep convective cloud systems are one of the leading contributors to weather related disasters, provide much of the fresh water used by society, and contribute significantly to the interactions among weather and climate. Convection is known to be influenced strongly by the characteristics of its environment, including the vertical structure of temperature, moisture, and wind. It has also been shown in many numerical modeling studies to be sensitive to the assumptions made in the representation of cloud processes.

 

This presentation will explore the relative influence of environmental (extrinsic) factors and cloud microphysical parameter (intrinsic) uncertainty in the evolution of tropical deep convection. The effect of both types of factor on the energy and water cycle, as well as on convective dynamics and heating, are shown. Ensemble Monte Carlo experiments quantify convective storm sensitivity, while ensemble data assimilation experiments provide traceability from convective outcomes to control factors. The results have implications for modeling, data assimilation, and the design of future observing systems.

How to cite: Posselt, D.: The relative sensitivity of convective simulations to perturbations in initial conditions and microphysics parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8429, https://doi.org/10.5194/egusphere-egu23-8429, 2023.

X5.24
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EGU23-5714
Tomislava Vukicevic, Derek Posselt, and Srdjan Jurlina

It has long been known that microphysics parameterizations are among leading sources of model uncertainty in storm and convective scale weather prediction.  The uncertainty results from combination of imperfect knowledge of the microphysics processes, inability to explicitly resolve them at computationally feasible spatial and phase-space resolutions, as well as from inherent limited predictability of micro to turbulent scale processes.   Representing these in the context of improving probabilistic prediction skill using ensembles has been the subject of many studies, but remains an outstanding problem.  The problem is especially acute in storm and convective scale ensemble prediction, where there may be strong coupling of errors between ensemble data assimilation and forecasting. 

Over the last decade, the inclusion of stochastic representation of model uncertainty associated with physical parameterizations has emerged as a viable approach for representing the intrinsic uncertainties of the microphysical parameterizations.  This study examines sensitivity of storm scale ensemble simulations to representation of microphysics parameterization uncertainties using a cloud resolving model.  We compare several stochastic parameter (SP) perturbation methods, including various parameter distributions and parameter covariance models, applied to physical parameters in a bulk microphysics parameterization.  The study follows a prior study, in which a 1D column version of the 3D cloud resolving model was used to test non-stochastic and several SP perturbation methods for which the parameter perturbation statistical distributions were based on Markov Chain Monte Carlo (MCMC) inversions with synthetic observations. That study indicated that SP schemes produce significantly more ensemble variance of microphysics states than non-stochastic, and that inclusion of parameter covariances, and specifically those that vary with the state of the system, improve their performance.

The current study investigates impacts of SP scheme configurations on microphysics with dynamical feedbacks in 3D ensemble simulations.  The statistical parameter distributions used for the SP scheme are obtained as in the 1D study using MCMC inversions with synthetic observations. The results are evaluated in terms of changes to the ensemble mean and variance of microphysical and dynamical states and the simulated column integral microphysics-sensitive satellite-based observable quantities. We discuss the results and note the implications for convective scale ensemble data assimilation and forecasting. 

How to cite: Vukicevic, T., Posselt, D., and Jurlina, S.: Evaluation of stochastic parameter representation of microphysics parameterization uncertainty for convective scale ensemble data assimilation and prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5714, https://doi.org/10.5194/egusphere-egu23-5714, 2023.

X5.25
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EGU23-7868
Tijana Janjic, Yvonne Ruckstuhl, and Stefanie Legler

Parametrization of microphysics as well as parametrization of processes in the surface and boundary layers typically contain several tunable parameters. The parameters are not observed and are only crudely known. Traditionally, the numerical values of these model parameters are chosen by manual model tuning, leading to model errors in convection permitting numerical weather prediction models. More objectively, parameters can be estimated from observations by the augmented state approach during the data assimilation or by combing data assimilation with machine learning (ML).

If the parameters are updated objectively according to observations, they are flexible to adjust to recent conditions, their uncertainty is considered, and therefore the uncertainty of the model output is more accurate. To illustrate benefits of online augmented state approach, Ruckstuhl and Janjic (2020) show in an operational convection-permitting configuration that the prediction of clouds and precipitation is improved if the two-dimensional roughness length parameter is estimated. This could lead to improved forecasts of up to 6 h of clouds and precipitation. However, when parameters are estimated by the augmented state approach, stochastic model for the parameters needs to be pre-specified to keep the spread in parameters. Alternatively, Legler and Janjic (2022) investigate a possibility of using data assimilation for the state estimation while using ML for parameter estimation in order to overcome this problem. We train two types of artificial neural networks as a function of the observations or analysis of the atmospheric state.  The test case uses perfect model experiments with the one-dimensional modified shallow-water model, which was designed to mimic important properties of convection. Through perfect model experiments we show that Bayesian neural networks (BNNs) and ensemble of point estimate neural networks (NNs) are able to estimate model parameters and their relevant statistics. The estimation of parameters combined with data assimilation for the state decreases the initial state errors even when assimilating sparse and noisy observations.

How to cite: Janjic, T., Ruckstuhl, Y., and Legler, S.: Learning model parameters from observations by combining data assimilation and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7868, https://doi.org/10.5194/egusphere-egu23-7868, 2023.

X5.26
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EGU23-4910
Mozhdeh Jamei and Ebrahim Asadi Oskouei

Satellite observations play an important role in providing the initial conditions for the Numerical Weather Prediction (NWP) models. Satellite data are assimilated into the first estimate provided by NWP models using a radiative transfer model. The impact of satellite observations significantly depends on the accuracy of the simulation performed by the radiative transfer (RT) models. In recent years, there have been significant advances in RT modeling for microwave and infrared observations, which are not sensitive to the surface. However, in the case of sensitive surface observations such as Soil Moisture and Ocean Salinity (SMOS) satellite observations, the assimilation has been limited by inaccuracy in the forward calculations. This study investigates the accuracy of the L-band Microwave Emission of the Biosphere (L-MEB) RT model for SMOS frequencies using high-quality in-situ observations as input. The L-MEB model is the forward RT model used in the SMOS L2 algorithm, specifically developed to simulate brightness temperature (TB) over the land surfaces at different incidence angles (between 0° and 60°). The L-MEB model simulated the SMOS TB data with the horizontal (H) and vertical (V) polarization at the lowest SMOS incidence angles at the meteorological stations over Iran.The land cover at these stations is either bare soil or low vegetation. The comparison between simulated TB and the SMOS TB products showed a suitable RMSE and a relatively low bias for horizontally and vertically polarized channels. The relatively low bias can justify the assimilation of SMOS observations into the data assimilation systems. However, cross-comparison of the RT models used at the NWP centers and the RT models such as L-MEB, which were mainly developed to work with the SMOS data, is required to ensure that the operational RT models used at the NWP centers meet the same accuracy.

 

How to cite: Jamei, M. and Asadi Oskouei, E.: Evaluating the L-MEB forward radiative transfer model for the assimilation of SMOS observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4910, https://doi.org/10.5194/egusphere-egu23-4910, 2023.

X5.27
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EGU23-13441
Hyemin Shin, Jeon-Ho Kang, and In-Hyuk Kwon

The Aeolus Atmospheric Laser Doppler Instrument (ALADIN) sensor onboard Aeolus provides the Horizontal Line Of Sight (HLOS) wind. The satellite-based HLOS wind profile data is significant because it complements the southern hemisphere, tropical and polar regions where existing wind observations are insufficient. The KMA also assimilates the HLOS wind for the operational data assimilation (DA) system since 2021, showing slightly positive impacts on average in the analysis field. However, it was confirmed that the impacts were relatively lower than those of the leading centers and limited due to systematic or random errors in the observation. 
In this study, we tested if we could enhance the positive impacts by applying the bias correction (BC) method to the HLOS wind observation. To this end, the Total Least Squares (TLS) were tested to conduct on the KIM Package for Observation Processing (KPOP) system, which is a system to provide well-qualified observations to the DA system. It shows better statistics in the mean and standard deviation of the first guess departures (O-B) by applying the TLS BC method with -0.19 m/s and 3.30 m/s from -0.44 m/s and 6.22 m/s, relatively. Detailed impacts on the analysis and forecast fields from the cycling experiments will be presented.

How to cite: Shin, H., Kang, J.-H., and Kwon, I.-H.: Impact study of Aeolus/ALADIN bias correction in the KIM data assimilation system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13441, https://doi.org/10.5194/egusphere-egu23-13441, 2023.

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EGU23-3105
Yu Li, Tran Vu La, Ramona-Maria Pelich, Marco Chini, Patrick Matgen, and Christophe Messager

Convective system (CS) is an extreme weather event occurring regularly over the subtropical and tropical regions such as the Gulf of Guinea, the Gulf of Mexico, Lake Victoria, Southeast Asia, India, and Australia. Certain CS types, i.e., mesoscale CS, supercell convective storms, squall lines, are disastrous for human life, infrastructures, and economic activities since they can produce strong surface winds, heavy rainfall, and significant lightning. Over the last decades, the CS observing, monitoring, and forecasting have been much improved thanks to a dense network of GEOstationary (GEO) satellites, including Meteosat, GEOS, Himawari, and Gaofen, covering Europe, Africa, America, and the Asia Pacific, respectively. However, the observation and prediction of the extreme weather events associated with deep convection are still a big challenge since they often occur suddenly, develop quickly, and become intense in a short time (several hours). Such unpredicted features are a significant issue for the numerical weather prediction models. While the prediction of intense rainfall associated with deep convection is still ongoing, the estimation of surface convective wind gusts has some important advancements. La et al. [1-2] indicated Sentinel-1 C-band Synthetic Aperture Radar (SAR) data with a high spatial resolution and wide swath bring significant advantages for observing and estimating ocean surface convective wind gusts. Indeed, through the images acquired by the Sentinel-1 Low Earth Orbit (LEO) satellite, one can observe convective wind patterns at both mesoscales and sub-mesoscales, as well as wind hot spots (15-25 m/s) at a small scale. The studies [1-2] also showed the relationship between surface wind patterns and deep convective clouds observed on Meteosat GEO images. In particular, the collocation of Sentinel-1, Aeolus Lidar, and Meteosat devices [3] enabled a multi-dimensional view of deep convection and its vertical and horizontal dynamics.

Following the previous studies, we illustrate in this paper more interesting cases of multi-dimensional CS observations by the collocated GEO and LEO sensors. They include sea surface convective wind patterns observed by Sentinel-1 LEO, intense downdrafts detected by Aeolus Lidar LEO, and deep convective clouds observed by Meteosat GEO. These cases expected to strengthen the relationship between deep convection and strong surface winds over the sea. In particular, we present the assessment of surface convective wind gust estimates through comparisons to in situ wind measurements by the moored buoys and weather stations. This work is a significant step to strengthen the conclusion that the high-intensity radar backscattering observed on Sentinel-1 C-band SAR images is associated with surface convective wind gusts rather than induced by precipitation.

[1] T. V. La and C. Messager, "Convective System Observations by LEO and GEO Satellites in Combination," IEEE JSTARS, vol. 14, pp. 11814-11823, 2021, doi: 10.1109/JSTARS.2021.3127401.

[2] T. V. La and C. Messager, "Different Observations of Sea Surface Wind Pattern Under Deep Convection by Sentinel-1 SARs, Scatterometers, and Radiometers in Collocation," IEEE JSTARS, vol. 15, pp. 3686-3696, 2022, doi: 10.1109/JSTARS.2022.3172375.

[3] La, T. V., & Messager, C. (2021). Convective system dynamics viewed in 3D over the oceans. Geophysical Research Letters, 48(5), e2021GL092397.

How to cite: Li, Y., La, T. V., Pelich, R.-M., Chini, M., Matgen, P., and Messager, C.: Insight of deep convection and sea surface wind gusts link through collocated GEO and LEO data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3105, https://doi.org/10.5194/egusphere-egu23-3105, 2023.

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EGU23-11119
Nikolaos S. Bartsotas, Olga Sykioti, Christos Spyrou, Kostas C. Douvis, Vassilis Amiridis, Christos Zerefos, and Stavros Solomos

A broad spectrum of environmental processes such as radiation, cloud formation, ocean fertilization and human health are affected from the presence of mineral dust. The transport of dust particles is dictated by the prevailing meteorological conditions as well as the composition and physiochemical properties of the particles themselves. Which, in turn, are bound to the soil mineralogy at the source region.

Numerical weather prediction models can estimate the transport of dust particles, yet a more refined mineralogical categorization can significantly improve the dust transport estimations and  increase preparedness for implications on weather, biogeochemistry and health. This novel mineralogical representation is derived from multi-spectral satellite remote sensing sensors (Sentinel 2A) over a limited area around Lake Chad in Sahara desert by taking into account dust particle characteristics such as size, composition and optical properties. The mineralogy map will be implemented in WRF/CHEM model to improve the accuracy of atmospheric simulations. The final product will be juxtaposed against current state-of-the-art mineralogical products such as the NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission. Dust transport simulations will be compared against field measurements from Antikythera PANGEA station in the Mediterranean and ASKOS campaign in the Atlantic Ocean.

How to cite: Bartsotas, N. S., Sykioti, O., Spyrou, C., Douvis, K. C., Amiridis, V., Zerefos, C., and Solomos, S.: The development of a detailed mineralogical database from satellite remote sensing products, towards an improved representation of dust transport in NWP simulations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11119, https://doi.org/10.5194/egusphere-egu23-11119, 2023.