Enter Zoom Meeting

AS1.9

EDI
Developments in storm and convective scale data assimilation and observations

Storm and convective scale weather data analysis and prediction still present significant challenges for atmospheric sciences. Addressing them requires synergy of advances in observing at these scales and in data assimilation with convective scale models. This session invites contributions from developments in
• Convective scale data assimilation techniques
• Model uncertainty representation in convective scale data assimilation
• Remote sensing observations at convective scales: data products, observing strategy, and technology
• Assessment of the impact of convective scale data assimilation development, and new observations, on prediction

Convener: Tomislava Vukicevic | Co-conveners: Tijana Janjic, Derek J. Posselt, Masashi Minamide
Presentations
| Fri, 27 May, 15:10–16:31 (CEST)
 
Room 1.34

Fri, 27 May, 15:10–16:40

Chairpersons: Tomislava Vukicevic, Tobias Necker

15:10–15:20
|
EGU22-2898
|
solicited
|
Virtual presentation
Sarah L. Dance

Convection-permitting (km-scale) data assimilation systems have been used in research and operational numerical weather prediction for more than fifteen years. These systems have been proven to provide improved short-term (0-36 hour) nowcasts, particularly for hazardous weather such as convective storms and fog.  However, there are still many challenges to be addressed in these high-resolution systems.  We will briefly review these broad challenges including multiscaling, spin-up, nonlinearity and model error.

For the main focus of the presentation, we will consider the challenge of providing detailed observation information on appropriate scales in the analysis. We will discuss the treatment of observation and background error covariances and show how they influence the scales in the analysis in idealized studies.  We find that dense observations are most beneficial when they provide a more accurate estimate of the state at smaller scales than the prior estimate. In our idealized experiments, this is achieved when the length-scales of the observation-error correlations are greater than those of the prior estimate and the observations are direct measurements of the state variables. We further test these ideas in an operational system, by assimilating Doppler radar wind observations taking account of their spatially correlated observation errors.  The approach taken gives results for the scales represented in the analysis increments that are consistent with the findings from the idealized studies. In particular, we find that using the correlated observation-error statistics with denser observations produces increments with shorter length-scales than the control. Furthermore, the use of dense Doppler radar wind observations with spatially correlated errors provides improvements in forecast skill, particularly for forecasts of intense convective rainfall, without increasing the wall-clock time for the assimilation.  

Finally, we will discuss the potential of novel observation types such as opportunistic data and those obtained from crowdsourcing to fill some of the gaps in the observation network. We take vehicle-based temperature observations as an example. We discuss the instrument and representation uncertainties associated with vehicle-based observations and present some results from a proof-of  concept trial.  Despite the low precision of the trial data, our results show the potential of vehicle-based observations as a useful source of spatially-dense and temporally-frequent observations for numerical weather prediction.  

How to cite: Dance, S. L.: Observations and multiple scales in convection permitting data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2898, https://doi.org/10.5194/egusphere-egu22-2898, 2022.

15:20–15:27
|
EGU22-1361
|
ECS
|
On-site presentation
Lukas Kugler et al.

Although cloud-affected satellite observations provide a promising source of information for convective-scale NWP, they are still rarely used in operational assimilation systems. This reveals that we do not fully understand the challenges involved in their assimilation as e.g. observation operator non-linearity, the non-linear evolution of clouds and unresolved scales of the model forecast. To mitigate these issues, we test various approaches for the assimilation of cloud-affected satellite observations in idealized simulations, i.e. within an observing system simulations experiments (OSSE) framework. We apply superobbing and thinning to visible and infrared observations, assimilate cloud-cover instead of radiance observations and study their effect on nonlinearity, aiming to linearize the relationship between observation and state variables and thus improve the assimilation procedure. We assimilate deep-convective systems in a 2-km Weather Research and Forecasting (WRF) model using the Data Assimilation Research Testbed's (DART) Ensemble Adjustment Kalman Filter with its novel interface to the radiative transfer model RTTOV. The latter includes MFASIS, a recently developed computationally efficient observation operator for satellite reflectances in the visible range.

How to cite: Kugler, L., Pierotti, N., Serafin, S., and Weissmann, M.: Assimilating cloud-affected visible & infrared satellite observations in idealized simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1361, https://doi.org/10.5194/egusphere-egu22-1361, 2022.

15:27–15:34
|
EGU22-4413
|
On-site presentation
Tobias Necker et al.

Appropriate localization is crucial for the success of ensemble data assimilation systems. Localization mitigates sampling errors and damps long-range spurious correlations, which arise from modeling background error covariances using small ensembles. However, finding the best localization function and scale is challenging. Recent studies showed that an optimal localization can depend on various factors such as the atmospheric conditions, the variable of interest, ensemble size, or observation type. Our goal is to improve localization for convective scale data assimilation based on a convection-permitting 1000-member ensemble simulation. The data set covers several forecasts in a high-impact weather period in summer 2016 (Necker et al. 2020a & 2020b). Our latest study aims at finding optimal localization functions and scales in the vertical. We focus on 40-member subsamples and assume the 1000-member ensemble covariance as truth. We estimate optimal localization length scales based on the often-applied Gaspari-Cohn function. Furthermore, we compare the performance of different tapering functions including an exponential-shaped function. The first results indicate that other tapering functions can outperform the Gaspari-Cohn function in the vertical. Optimal localization scales strongly vary between different weather situations. Overall, our analysis assesses covariances and localization between different variables and/or observations covering both model and observation space. This experimental design enables general conclusions independent of a specific data assimilation algorithm.

How to cite: Necker, T., Hinger, D., Weissmann, M., and Miyoshi, T.: Guidance on optimal vertical covariance localization based on a convection-permitting 1000-member ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4413, https://doi.org/10.5194/egusphere-egu22-4413, 2022.

15:34–15:41
|
EGU22-4841
|
Virtual presentation
Tijana Janjic and Yuefei Zeng

Often physical properties of a system that we are modeling dictate plausible values of the initial conditions of our numerical models. Unfortunately, by using modern data assimilation techniques as the ensemble Kalman filter algorithm to obtain these initial conditions, physical property of non-negativity is frequently violated. To mitigate this sign problem and to simultaneously maintain the mass conservation, a new concept of combining weak constraints on mass conservation and non-negativity has been introduced in our recent paper (Janjic and Zeng 2021), with a focus on hydrometeor variables in convective-scale data assimilation. The algorithm is fast, easy to implement modification of the local ensemble transform Kalman filter that is able to weakly preserve both properties of mass conservation and non-negativity. In idealized experiments that assimilate radar data in non-hydrostatic, convection-permitting numerical model and update hydrometeor values, we show the benefit of the proposed approach on prediction of atmospheric water variables. Results show that both weak constraints successfully improve the mass conservation property in analyses and both reduce the biased increase in integrated mass-flux divergence and vorticity. Furthermore, the least biased increase is obtained by combining both constraints, and the best forecasts are also achieved by the combination.

Janjić, T., & Zeng, Y. (2021). Weakly constrained LETKF for estimation of hydrometeor variables in convective-scale data assimilation. Geophysical Research Letters, 48, e2021GL094962

How to cite: Janjic, T. and Zeng, Y.: Weakly Constrained LETKF for Convective-Scale Data Assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4841, https://doi.org/10.5194/egusphere-egu22-4841, 2022.

15:41–15:48
|
EGU22-8198
|
Virtual presentation
Yuxuan Feng et al.

For convective clouds and precipitation, model uncertainty in cloud microphysics is considered one of the most significant sources of model error. In our recent paper (Feng et al. 2021), samples for model microphysical uncertainty are obtained by calculating the differences between simulations equipped with two- and one-moment schemes during a one-month training period. The samples are then added to convective-scale ensemble data assimilation as additive noise and combined with large-scale additive noise based on samples from climatological atmospheric background error covariance. Two experiments, including the combination and large-scale error only, are conducted for a one-week convective period. The results reveal that the simulation with a two-moment scheme triggers more convection and has larger ice-phase precipitation particles, which produce a stronger signal in the melting layer. During data assimilation cycling, although more water is introduced to the model, it is shown that the combination performs better for both background and analysis and significantly improves short-term ensemble forecasts of radar reflectivity and hourly precipitation.

Feng, Y., Janjić, T., Zeng, Y., Seifert, A., & Min, J. (2021). Representing microphysical uncertainty in convective-scale data assimilation using additive noise. Journal of Advances in Modeling Earth Systems, 13, e2021MS002606.

How to cite: Feng, Y., Janjic, T., Zeng, Y., Seifert, A., and Min, J.: Representing Microphysical Uncertainty in Convective-Scale Data Assimilation Using Additive Noise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8198, https://doi.org/10.5194/egusphere-egu22-8198, 2022.

15:48–15:55
|
EGU22-2124
|
On-site presentation
Ziad Haddad et al.

Microwave radiometers and radars in low Earth orbit (LEO) are sensitive to the amount of condensed water in clouds. However, their temporal sampling is quite limited – a single LEO instrument will very rarely observe a weather system more than once during the lifetime of the system. Recent technological advances have enabled the design of miniaturized microwave instruments that are quite capable and, at the same time, inexpensive enough to consider the formation of a convoy of identical radars or radiometers in low-Earth orbit, separated in time by a very short interval, on the order of a minute, the temporal scale required to observe the highly nonlinear cloud dynamics. The time sequences of observations are conceptually similar to the loops that are currently obtained from ground weather radar, as well as geostationary imagery, which readily show the evolution of precipitation (in the radar case) or cloud tops (in the imagery case) over minutes. The satellite convoys overcome the limitations of geostationary images (which are sensitive only to the very top of the clouds), and those of ground radar (with its very limited spatial coverage and its insufficiently short interval between consecutive scans). Because each satellite instrument is sensitive to the 3-dimensional distribution of condensed water within its field of view, the convoy is sensitive to the change in this condensed water over the minute(s) separating the convoy members. NASA’s recently selected INCUS mission will be the first project to demonstrate this new concept, with a convoy of three identical Ka-band reflectivity profiling radars, along with a five-channel microwave radiometer.

We have conducted analyses based on simulations – as well as observations from ground-based zenith-pointing profilers – to quantify the ability of a convoy made up of a pair of small Ka-band radars that measures reflectivity only, or a pair of mm-wave radiometers that measures microwave radiances in several mm-wavelength channels, to detect convective updrafts above the freezing level and to quantify their intensity. In the case of a pair of radars, one can retrieve the vertical profile of the vertical transport in the portion of the column where the condensed water concentration is above a minimum threshold (of about 0.05 g/m3 in our analyses) and the vertical velocity exceeds a minimum threshold (of about 2 m/s) with quite low uncertainty (whose characteristics depend on the coarse shape of the vertical velocity as a function of height). These new observation strategies are not only useful to evaluate and improve the model representation of vertical transport in convective storms, they are also uniquely useful to quantify a currently “missing link” in the Hadley circulation, in establishing the potential-energy contribution by an individual convective system to the Upper Troposphere / Lower Stratosphere bubble of high-entropy air mass.

Acknowledgement: This research was carried out at Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration

How to cite: Haddad, Z., Sawaya, R., Prasanth, S., van den Heever, M., Sy, O., van den Heever, S., Grant, L., Rao, T. N., Stephens, G., Hristova-Veleva, S., Posselt, D., and Storer, R.: Observation strategy of the INCUS mission: retrieving vertical mass flux in convective storms from low-earth-orbit convoys of miniaturized microwave instruments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2124, https://doi.org/10.5194/egusphere-egu22-2124, 2022.

15:55–16:02
|
EGU22-850
|
Virtual presentation
Derek J. Posselt et al.

It has long been known that model physics uncertainty can contribute as much or more to errors in forecasting and data assimilation as errors in initial conditions. Many studies have attempted to include the effects of model physics uncertainty in data assimilation by introducing static perturbations to model parameters. In such studies, parameter values are modified at the beginning of a simulation and remain unchanged throughout the duration of the forecast. Uncertainty is spanned by generating an ensemble of forecasts, each member having a different set of parameter values. Other studies have implemented dynamic perturbations to parameters, introducing methods that modify parameter values online in a stochastic fashion.

 

We present here the results of a study that investigates the sensitivity of convective cloud structures to static and stochastic cloud microphysical parameter perturbations. Static parameter values are drawn from a database produced by a Markov chain Monte Carlo algorithm, while stochastic perturbations are applied via a stochastically perturbed parameterization (SPP) scheme. Both static parameter perturbations and SPP are applied to multiple microphysical parameters within a Lagrangian column model, used in several prior published studies. The 1D column microphysics model is forced with prescribed time-varying profiles of temperature, humidity and vertical velocity in such a way as to emulate the environment inside of a convective storm. This modeling framework allows for investigation of the effect of changes in model physics parameters on the model output in isolation from any feedback to the cloud-scale dynamics. 

 

The results are evaluated in terms of changes to the ensemble mean and variance of time evolving profiles of hydrometeor mass quantities, the microphysics processes within the model as well as in terms of the simulated column integral microphysics-sensitive satellite-based  observables. The outcomes of our experiments indicate a high degree of sensitivity of the to the way in which the SPP scheme is implemented. In particular, the distributions from which parameters are drawn, as well as the decorrelation time scale, have a large effect on the simulation outcomes. We discuss the results of SPP, compare with our static perturbation experiments, and note the implications for convective scale data assimilation. 

How to cite: Posselt, D. J., Vukicevic, T., and Stankovich, A.: Including Cloud Microphysics Uncertainty in Convective Data Assimilation: Stochastic vs Static Parameter Perturbations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-850, https://doi.org/10.5194/egusphere-egu22-850, 2022.

16:02–16:09
|
EGU22-8448
|
ECS
|
Virtual presentation
Masashi Minamide and Derek Posselt

Prediction of rapidly intensifying tropical cyclones (TCs) have been a challenging topic. Because most TCs are born and develop over tropical oceans with limited in-situ observation networks and infrequent low Earth orbiting satellite overpasses, geostationary satellite observations are often the only available information to capture the lifecycle of TCs.

In this study, the impacts of assimilating all-sky satellite radiances from GOES-16, together with the set of conventional observations, on the prediction of the rapid intensification process of TCs are examined using convection permitting ensemble Kalman filter data assimilation system originally developed at Penn State University with WRF and CRTM. We have conducted assimilation experiments for 2017 Atlantic hurricane season. The assimilation of all-sky satellite radiances contributed to better constraining the dynamic and thermodynamic state variables, which helped to capture the developing convective activity within the inner-core region of TCs. The TC intensity forecast error was reduced by roughly 20 % at the peak time. We found that the all-sky satellite radiances contributed to more than 90 % of error reduction. This study will provide implications about what are the sources of uncertainty in predicting rapidly intensifying TCs, as well about the design of future observation networks tasked with better initializing and predicting developing TCs.

How to cite: Minamide, M. and Posselt, D.: Convection-Permitting Ensemble All-sky Satellite Radiance Assimilation for the Prediction of Rapidly Intensifying Tropical Cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8448, https://doi.org/10.5194/egusphere-egu22-8448, 2022.

16:09–16:16
|
EGU22-8835
|
On-site presentation
William Blackwell

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) 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 50 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 six 3U CubeSats (5.4 kg each) in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer 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 and measurement sensitivity is comparable with current state-of-the-art observing platforms. Launches for the TROPICS constellation mission are planned in the first half of 2022. 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 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 highlight numerous technical innovations that have made the TROPICS mission possible and enabled new capabilities for future earth observing missions.

How to cite: Blackwell, W.: On-orbit Results from the NASA TROPICS Pathfinder Constellation Precursor Mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8835, https://doi.org/10.5194/egusphere-egu22-8835, 2022.

16:16–16:31
Discussion