In recent year’s southern state of India, Karnataka, has witnessed many catastrophic rainfall events. These events have caused enormous loss of life, property and crops across the State. In the year 2019, till the month of August, state was facing drought like condition because of prolonged dry spell in pre-monsoon (March-May) and south-west monsoon (June-July) season. During 06 – 10 August state has received average rainfall of 224 mm whereas some parts of the state received heavy rainfall (2493 mm) due to deep depressions over the Bay of Bengal. This study aims to evaluate the impact of lead time and three dimensional variational (3DVAR) data assimilation in simulation of heavy rainfall events during this period using Weather Research and forecasting (WRF) model. The model is configured with 3 nested-domains having high-resolution over the Karnataka State. The high resolution forecasts over Karnataka are evaluated against high resolution (~4 km) in-situ telemetric rain-gauge observations to assess model performance. These events are simulated using initial and boundary conditions from Global Forecast System (GFS) data. Lead time effect is analyzed by initializing model at 1200 UTC (12 hours prior to event day) and at 0000 UTC (event day) and the model is integrated for 48 hours duration. The impact of 3DVAR data assimilation is examine by comparing forecasts with assimilation of data from various sources like balloon, satellite, ground station and buoy (AIRS, MODIS, BUOY, TWS, ASCAT, WINDSAT, SSMIS and Radiosonde) against control experiment (without data assimilation). The results show that the model is able to capture the high intensity observed rainfall though location errors are there in many cases. It is note that model skill is sensitive to lead time and model performance for different lead time varied from case to case. Simulations with assimilation of observations in initial condition improved the forecasts compared to control simulations. The model skill (Bias Score, Threat Score and Heidke Skill Score) is better in simulations with data assimilation. 

How to cite: Bankar, A. and Vasudevan, R.: Simulation of Extreme Rainfall Events over Karnataka, Southern state in India: Impact of Lead Time and Data Assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-130, https://doi.org/10.5194/egusphere-egu22-130, 2022.

The ensembles used to produce probabilistic weather forecasts are limited by the availability of computational resources. This can lead to large sampling error and poorly resolved ensemble distributions. Furthermore, the expense of large ensembles makes it difficult to determine how many members would be needed to achieve a desired level of sampling uncertainty. A 100,000 member ensemble from a 1-dimensional idealised prediction system which replicates the key processes of convection is developed to examine how sampling error of random variables converges with ensemble size. Distributions of the three prognostic variables, evolving over 24 hours of a free-run, are found to correspond to the three categories of distribution that were identified in a study of a 1000-member NWP ensemble, indicating that the idealised model can represent key aspects of the forecast uncertainty. Bootstrap samples from the 100,000-member distributions are used to obtain widths of the 95% Confidence Interval of various sampling distributions, as function of ensemble size n. For sufficiently large ensemble size, the confidence intervals were found to decrease proportional to n^-1/2. This scaling is universal for the mean, variance, skewness, kurtosis and several quantile random variables. The sampling error depends on distribution shape and the random variable. Techniques using parameterisation and multiple small ensemble computations are also investigated as methods to allow convergence to be estimated using smaller ensembles.

How to cite: Tempest, K., Craig, G. C., and Brehmer, J. R.: Asymptotic convergence of sampling uncertainty in a 100,000 member ensemble using an idealised model of convection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1339, https://doi.org/10.5194/egusphere-egu22-1339, 2022.

In this presentation, results will be discussed from a series of tests that were performed with the FV3-LAM model using 25, 13, and 3 km horizontal grid spacing, and two physics suites, to simulate the August 10, 2020 Midwestern Derecho, the most damaging single thunderstorm event in U.S. history. The two physics suites resemble those used in the HRRR model (referred to as RRFS, Rapid Refresh Forecast System) and the GFS model.

This derecho was poorly forecast by most models in the days and even hours before the event occurred. Only some hourly runs of the HRRR and an experimental version of the HRRR the night before correctly captured an intense bowing line of storms occurring on August 10. Therefore, experimental HRRR output from 00 UTC was used to initialize and provide lateral boundary conditions to the FV3-LAM runs. Runs were performed with and without the Grell-Freitas convective parameterizations in the RRFS suite for all grid spacings.

It was found that both the 13 km and 25 km runs that did not use convective parameterizations did a good job showing very intense convection in the correct area and time. When the convective schemes were turned on, the 25 km results were degraded, but the 13 km results did not change much. However, when grid spacing was refined to 3 km, neither runs with the RRFS or GFS physics suites simulated the derecho. The big difference from the coarser grid spacing runs was that anomalous convection formed during the night in the 3 km runs, removing the convective available potential energy, and not allowing substantial convection to form during the day on August 10. Instead, the stronger storms were well to the south and east of Iowa. Although this was a common problem with many convection-allowing models run in real time when the event occurred, this result is potentially troubling since the experimental HRRR run that provided the initial and lateral boundary conditions used the same grid spacing of 3 km, but did not produce the anomalous convection at night and thus correctly showed the intense mid-day derecho. The spurious convection in FV3-LAM seems to be due to stronger ascent prior to initiation of the spurious nocturnal convection than was present in the HRRR.  Of note, when the Grell-Freitas deep and shallow convective schemes are turned on in the 3 km FV3 run, the spurious convection is eliminated and the simulation is remarkably accurate, producing an intense derecho with over 30 m s-¹ sustained winds at 10 m, with gusts to 45 m s^-1, in the same general location at the same time as the observed event.  The use of the convective scheme results in a layer around 720 hPa with 1-2 C of warming around the time that spurious convection had formed in the 3 km run lacking the convective scheme.  This modest warming in a narrow layer is sufficient to prevent the spurious convection, completely changing the forecast of the daytime derecho from an absolute failure to a remarkable success.

How to cite: Gallus, W. and Harrold, M.: Unusual behavior in FV3-LAM simulations of the Midwestern U.S. Derecho of August 10, 2020: forecast degradation with improved resolution and a need for a convective parameterization with 3 km grid spacing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1836, https://doi.org/10.5194/egusphere-egu22-1836, 2022.

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so do potentially uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties on the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.

Here, we focus the investigation on the effects of parametric uncertainty in a perturbed parameter ensemble, using the ICON model (with 2-moment cloud scheme, at 1 km grid spacing). A latin hypercube sampling is used to generate systematic variations of selected microphysical parameters from an eight-dimensional parameter space. Considered processes include riming, diffusional growth of ice and snow, CCN and INP activation, as well as the mass-diameter and mass-velocity relations. Isolated sensitivity experiments show distinct influences of all parameters on hail related variables, where the strongest impacts are found in simulations with reduced CCN and INP activation. We will present a detailed analysis of the simultaneous influence of parameter perturbations on the cloud microphysical evolution of the storm.

How to cite: Kuntze, P., Miltenberger, A., Hoose, C., Kunz, M., and Frey, L.: Impact of microphysical uncertainty on the evolution of a severe hailstorm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2461, https://doi.org/10.5194/egusphere-egu22-2461, 2022.

The successful development of numerical weather prediction (NWP) helps better preparedness for extreme weather events. Weather modifications have also been explored, for example, when enhancing rainfalls by cloud seeding. However, it is generally believed that the tremendous energy involved in extreme events prevents any attempt of human interventions to avoid or to control their occurrences.

In this study, we investigate the controllability of a chaotic dynamical system by adding small perturbations to generate amplified effects and to prevent extreme events. The high sensitivity to initial conditions would ultimately lead to modifications of extreme events with infinitesimal perturbations. Based on this idea, we extend the well-known observing systems simulation experiment (OSSE) and design the control simulation experiment (CSE) with the Lorenz-96 model, a widely-used toy system in data assimilation studies. We also study the sensitivity of the control to the amplitude of the perturbation, the forecast length, the localized perturbation and the partial observations. The CSE would be applicable to other chaotic dynamical systems including realistic numerical weather prediction models.

How to cite: Sun, Q., Miyoshi, T., and Richard, S.: Control Simulation Experiments with the Lorenz-96 Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3313, https://doi.org/10.5194/egusphere-egu22-3313, 2022.

In spite of the continuous improvement of numerical weather prediction (NWP) systems, thunderstorms remain hard to predict with accuracy. This difficulty partly results from a lack of observations to describe the initial state of the atmosphere. Total lightning is a good indicator of convective activity and lightning data assimilation could improve the prediction of thunderstorms, especially in regions where storm-related observations are scarce.

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellites will provide total lightning observations continuously over Europe with a spatial resolution of a few kilometers. This makes it a rich potential data source for convection-permitting NWP.

To prepare the assimilation of the flash extent accumulation (FEA) measured by LI in the French storm-scale regional AROME NWP system, a lightning observation operator is required to convert the model variables into a product comparable to the observations. Since LI FEA observations are not available yet (launch planned for the end of 2022), pseudo-LI FEA observations are generated from the records of the Météorage VLF ground-based lightning detection system (Erdmann et al., 2021).

Since neither flashes nor the electric field are predicted by the AROME model, the observation operator relies on proxy variables to link the flash observations to the prognostic variables of the model. This study focuses on the evaluation of different FEA observation operators from various proxies encountered in the literature and calculated from the outputs of 1 h AROME-France forecasts for 47 electrically active days in 2018.

Different regression techniques, linear regression as well as machine learning models, are used to relate the synthetic FEA and the modeled proxies. The data are processed as distributions over the whole domain (i.e. France) and time period since a pixel-to-pixel comparison exhibits a rather poor correlation. The training of the observation operator is performed on 44 days of the dataset and 3 days are used for the validation. The performances of each observation operator are evaluated by computing Fraction Skill Scores between synthetic FEA and proxy-based FEA. Two different proxy types emerged from the literature review: microphysical and dynamical proxies. The present study suggests that microphysical proxies seem to be more suited than the dynamical ones to model satellite lightning observations.

The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study.

How to cite: Combarnous, P., Erdmann, F., Caumont, O., Defer, E., and Martet, M.: An observation operator for geostationary lightning imager data assimilation in storm-scale numerical weather prediction systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4254, https://doi.org/10.5194/egusphere-egu22-4254, 2022.

In the last few years, Central Europe faced a number of severe, record-breaking heatwaves. Several previous studies focused on the predictability of such heatwaves on medium-range to subseasonal time scales (5 – 30 days). However, also short-term forecasts with up to 3 days lead time can exhibit substantial errors in the prediction of maximum temperatures (Tmax), even on larger spatial scales. This study investigates the causes of such short-term forecast errors in Tmax over Central Europe for the summers of 2015–2020, with an emphasis on heatwaves. For this purpose, 3-day forecasts of the 50-member ensemble of the operational ECMWF-IFS (ECMWF-EPS) are systematically compared against 0-18h control forecasts for the respective days of interest.

In general, ECMWF-EPS shows a tendency for too cold forecasts during heatwaves, particularly in situations with stagnant air masses under clear skies and weak synoptic forcing. A pattern correlation method and a multi-variate linear regression model are used to study the relative importance of different physical processes for 72h forecast errors in Tmax. It is found that errors in downwelling short-wave radiation (SWDS), mainly due to erroneous low cloud cover, are the dominant error source, particularly in a large-scale perspective and outside of heatwaves. Moreover, Tmax forecasts errors are more strongly linked to SWDS errors on days with too warm forecasts than on days with too cold forecasts.

Within heatwaves, other error sources gain importance; averaged over all summers 2015–2020, the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. An additional Lagrangian trajectory analysis for the summers 2018–2020 (limited availability of necessary ECMWF-EPS input data) suggests that these errors may be linked to accumulating errors in previous days' diabatic heating of near-surface air masses, much more so in heatwaves than on regular summer days. Such errors in diabatic heating history, which are substantially more important in northern and western parts of Central Europe, are on average consistent with prediction errors in air mass residence time over land and cloud cover traced along trajectories. On regional scales, other physical processes can be of dominant importance, but only during heatwaves. The coastal regions are most influenced by errors in near-surface wind (ventilation by cooler maritime air) whereas errors in soil moisture are most important in some regions of southeastern Central Europe.

In summary, short-range forecasts errors of summertime maximum temperatures over Central Europe are predominantly caused by over- or underestimation of short-wave irradiance. However, the dominance of this error source diminishes substantially during heatwaves, particularly on days where ECMWF-EPS underestimates Tmax. Such days are often stable and cloud-free and a decreased importance of SWDS is therefore not unexpected due to overall lower probability for substantial misprediction. Moreover, especially in heatwaves, Tmax forecasts may suffer from accumulated errors in diabatic heating of near-surface air. Their causes may partly be attributable to errors in air mass residence time over land and/or cloud cover along the trajectory path but further research is needed.

How to cite: Lemburg, A. and Fink, A. H.: Identifying causes of short-range forecast errors in maximum temperatures during recent Central European heatwaves using the ECMWF-IFS ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4339, https://doi.org/10.5194/egusphere-egu22-4339, 2022.

Funded jointly by NOAA’s National Weather Service (NWS) Office of Science and Technology Integration (OSTI) and the Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO), the UFS-R2O Project has made significant progress coordinating a large community of researchers, both inside and outside NOAA for integrating new research into the operational UFS applications.  The project began in July 2020 as a collaboration between the National Centers forEnvironmental Prediction (NCEP) EnvironmentalModelling Center, 8 NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL) and 6 universities and cooperative institutes. 

The project was conceived with a focus on leveraging the nascent UFS community to build new UFS applications that will replace several existing operational modeling systems and simplify the NCEP Production Suite (NPS).  The project consists of three integrated teams covering the global Medium Range Weather/Subseasonal to Seasonal (MRW/S2S); the regional Short Range Weather/Convection Allowing Modeling (SRW/CAM); and the Hurricane applications, and are supported by seven cross-cutting development teams shown in Figure 1. The MRW/S2S team is leading the development of a six-component global coupled (atmosphere/ocean/land/sea-ice/wave/aerosol) ensemble system targeted for combining the Global Forecast System (GFS) and the Global Ensemble Forecast system (GEFS) as a single application, the SRW/CAM team is leading the development of a regional hourly-updating high-resolution and convection-allowing Rapid Refresh Forecast System (RRFS) for prediction of severe weather, and the Hurricane team developing the Hurricane Analysis and Forecast System (HAFS) for high resolution global tropical cyclone predictions.  

Some of the highlights of the progress accomplished thus far include: (1) testing and evaluation of various prototype versions of the global coupled prediction system with incremental improvements to the component models and the coupling infrastructure; (2) development of a prototype coupled data assimilation system that can update the ocean, sea-ice, atmospheric and land states; (3) development of a limited-area convective-scale short-range ensemble prediction system that formed the basis for the RRFS; and (4) development of moving nest capability within the global or regional domains for the HAFS.

This presentation highlights the outcomes of the UFS R2O Project thus far, with emphasis on results from the UFS based coupled model deterministic and ensemble prototypes targeted for medium range and sub-seasonal weather forecasts.  We will also discuss on the reanalysis and reforecast strategies for sub-seasonal to seasonal prediction capabilities, and eventual development of the Seasonal Forecast System (SFS) that will replace the existing Climate Forecast System (CFSv2) in operations.

Figure 1: Structure and composition of the UFS-R2O Project

How to cite: Tallapragada, V., Whitaker, J., and Kinter, J.: NOAA's Unified Forecast System Research to Operations (UFS-R2O) Project for accelerated transition of UFS based forecast applications into operations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3101, https://doi.org/10.5194/egusphere-egu22-3101, 2022.

The South Atlantic Convergence Zone (SACZ) has been subjectively defined as a band of cloudiness from the intense convection over the Amazon basin extending toward southeast Brazil, that is one of the main components of the South American monsoon system. The SACZ represents a region of deep convection, causing heavy precipitation events in the region for at least 4 days. The precipitation that occurs during the months of October to March is essential for maintaining the climate of the Southeast, Midwest and North of Brazil. Because of this, SACZ is an important climatological feature of the austral summer in Brazil. The representation of SACZ precipitation is complex and the need for numerical models calibrated according to the atmospheric conditions of the region to be analyzed is increasing. Thinking on this, researchers from the National Institute for Space Research (INPE) have been developing the Brazilian Global Atmospheric Model (BAM), in order to improve weather and climate forecasting simulations and climate change studies. The BAM is a semi-implicit Eulerian spectral model (BAMb-SL version, approximately 1.0° x 1.0° of horizontal resolution). With the importance of SACZ in mind and the need to improve its prediction, this study aims to analyze the climatology of SACZ through simulations of the BAM model in the period between 1992 to 2015, in which 156 SACZ event were recorded. BAM simulations will be compared with observed and reanalysis data, in order to evaluate the performance of BAM simulating ZCAS. The data that will used in this study is the BAM simulations of the variables precipitation, 200-hPa wind, outgoing longwave radiation, and 850-hPa specific humidity, daily observed precipitation data from the dataset organized and interpolated to 0.25° x 0.25° grid by Alexandre C. Xavier and available on the website https://utexas.app.box.com/v/xavier-etal-ijoc-data, outgoing longwave radiation from Climate Prediction Center do National Oceanic and Atmospheric Administration (CPC – NOAA, spatial resolution 0.75º x 0.75º) and 200-hPa wind and specific humidity at the level of 850-hPa from ERA5 of the ECMWF (spatial resolution 0.30º x 0.30º). The analyzes were obtained from statistical methods, with the mean and monthly standard deviation of the accumulated precipitation, and mean monthly of the outgoing longwave radiation, 200-hPa wind and specific humidity at the level of 850hPa, applied which data sets that were explained. Overall, the initial results showed a good agreement between the data sets. The average accumulated precipitation presented by the model simulations represented the spatial distribution of precipitation, in the central region of the Brazil were characterizing the SACZ, but these values were lower compared to those observed. The lowest OLR values presented by the reanalyses on the central region of the Brazil characterizes the SACZ position, as well as the BAM simulations. The other variables are still being analyzed. With the results obtained untill now, it is possible to say that, although the magnitude of each variable is underestimated, the simulations showed a good level of agreement between the data sets in the spatial representation of the variables analyzed in the 156 SACZ events.

How to cite: Breasciani, C., Boiaski, N., Ferraz, S., and Herdies, D.: The South Atlantic Convergence Zone Represented by the BAM Model Simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4723, https://doi.org/10.5194/egusphere-egu22-4723, 2022.

Prediction of numerical weather prediction and climate models are the basis for informed decision making and increased resilience to hydrometeorological extremes in many of today’s resource management challenges e.g. in the agricultural sector. Coupled multi-compartment models are capable of reproducing interactions and feedbacks in the geosystem, and have thereby demonstrated versatile tools in a variety of applications. The Terrestrial Systems Modelling Platform (TSMP, https://www.terrsysmp.org) is an integrated regional Earth system model that simulates processes from groundwater across the land surface to the top of the atmosphere on multiple spatio-temporal scales. TSMP consists of the atmospheric model COSMO (Consortium for Small-scale Modeling), the CLM (Community Land Model), and the ParFlow hydrologic model, coupled through OASIS3-MCT. This work presents an evaluation of daily deterministic 10-day forecasts of the atmospheric, surface, and groundwater states and fluxes for a heterogeneous mid mountain-ranges area in the German and Belgium Eifel-Ardennes region in Central Europe from TSMP in a monitoring setup. TSMP runs at convection-permitting resolution of 1km (atmosphere) and 0.5km (sub- and land surface) over an area of 150km x 150km, driven by ECMWF HRES forecasts through a one-way double nest. Data from the densely instrumented Eifel/Lower Rhine Valley observational network of Terrestrial Environmental Observatories (TERENO, https://www.tereno.net) is used for evaluation of the TSMP simulations. TSMP forecasts from July 2019 to July 2021 covering an agricultural and hydrological drought and the transition back to the climatological mean state are analyzed in detail. Despite the complex terrain and the free running TSMP, meteorological and hydrological station data are generally well represented while a certain overestimation of daily precipitation is observed.

How to cite: Iakunin, M., Wagner, N., Graf, A., Goergen, K., and Kollet, S.: Evaluation of the daily forecasts from the coupled Terrestrial Systems Modelling Platform (TSMP) over a regional-scale domain in Central Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5186, https://doi.org/10.5194/egusphere-egu22-5186, 2022.

Despite the enormous potential of precipitation forecasts to save lives and property in Africa, the generally low skill has limited their uptake. Where the forecasts have been used, the low skill makes the forecast-based decisions questionable at best. In particular, the performance of the forecasts is spatially and temporarily variable and therefore should not be generalised. To improve the performance of the models, and hence, their uptake, validation, analysis of possible sources of predictability and post-processing should continuously be carried out.

Here we evaluate the quality of reforecast from the European Centre for Medium-range Weather Forecasting over Equatorial East Africa (EEA). The reforecasts are initialised twice a week with lead time up to 45 days and are available from the subseasonal-to-seasonal (S2S) data base at a spatial (temporal) resolution of 1.5° (6-hourly). The evaluation is done using both satellite (Integrated Multi-satellite Retrieval for Global Precipitation Measurement) and ground-based (rain gauges) rainfall observations for the period 2000–2019. Both the raw and post-processed reforecasts are analysed, from daily to sub-monthly lead times and for temporal aggregations (48-hours and 120-hours total precipitation). To assess the skill of the reforecasts, an existing ensemble probabilistic climatology (EPC) derived from the observations is used as the reference forecast (Walz et al. 2021, doi: 10.1175/WAF-D-20-0233.1). First results show that there is potential of skill in the raw forecasts up to 10 days ahead particularly in the elevated areas of EEA. There is positive skill in the forecast of rainfall occurrence and the full rainfall distribution, i.e., the Brier Skill Score and the Continuous Rank Probability Skill Score, are positive in most areas, especially over land. As expected the skill decreases with lead time, vanishing completely between day 10 and 15. Aggregating the reforecasts enhances the scores further, likely due to reduction in time and temporal mismatches. The skill also varies seasonally with the long rains in March-April-May (the major dry season in June-July-August) having the best (worst) skill over most parts of the region. The raw reforecasts have a systematic bias, being overconfident at all lead-times. To correct for this bias, post-processing the reforecast using the isotonic distributional regression (IDR) method is applied and the improvement in performance will be discussed. Overall, initial results indicate that raw and postprocessed ECMWF S2S forecasts over EEA have more skill compared to findings in related studies for northern tropical Africa.

How to cite: Ageet, S., H. Fink, A., Maranan, M., Walz, E.-M., and Schulz, B.: Predictability of rainfall in Equatorial East Africa from daily to sub-monthly time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5304, https://doi.org/10.5194/egusphere-egu22-5304, 2022.

In this study the transition from current practical predictability of midlatitude weather to its intrinsic limit is investigated. For this purpose, estimates of the current initial condition uncertainty of 12 real cases are reduced in several steps from 100% to 0.1% and propagated in time with a numerical weather prediction model (ICON at 40km resolution) that includes a stochastic convection scheme. It is found that the potential forecast improvement through initial condition perfection is 4-5 days, which can essentially be achieved with an initial condition uncertainty reduction by 90% relative to current conditions. With respect to physical processes, this reduction of the initial condition uncertainty is accompanied with a transition from rotationally-driven initial error growth to error growth dominated by latent heat release in convection and due to the divergent component of the flow. With respect to spatial scales, a transition from large-scale up-magnitude error growth to upscale error growth and an acceleration of the initial growth rate is found. Reference experiments with a deterministic convection scheme show a 5-10% longer predictability interval, but only if the initial condition uncertainty is small. These results confirm that planetary-scale predictability is intrinsically limited by latent heat release in clouds through an upscale-interaction process, while this process is unimportant on average for current amplitudes of the initial condition uncertainty.

How to cite: Selz, T., Riemer, M., and Craig, G.: The transition from practical to intrinsic predictability of midlatitude weather, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5312, https://doi.org/10.5194/egusphere-egu22-5312, 2022.

The behavior of two eddy-diffusivity mass-flux (EDMF) planetary boundary layer (PBL) schemes used in NOAA’s Global Forecast System is examined at the level of mixing processes.  The examination is performed by comparing the two schemes in 1-D simulations of convective PBL growth using the same physics configuration and two sets of initial atmospheric states extracted from three-dimensional (3-D) GFS initial conditions.  All simulations show that the TKE-EDMF scheme mixes more and leads to less CIN and CAPE than the Hybrid-EDMF scheme.  The excessive mixing of the TKE-EDMF scheme is consistent with that seen in the 3-D GFS forecasts compared with radiosonde data.  Diagnosis using process perturbation sensitivity experiments indicates that the mass-flux term is more dominant in the TKE-EDMF than in the Hybrid-EDMF scheme.  Quantitative aspects of the local eddy diffusivity are also different between the two schemes, pointing to uncertainty in the physical partition of local and non-local mixing in the EDMF formulation of the two schemes.  Additional sensitivity experiments show essential parameters that can be optimized according to observations and/or large-eddy-simulation results that provide a more realistic partition of local and non-local mixing.

How to cite: Bao, J.-W., Grell, E., Michelson, S., and Hong, S.: Process-level differences between two PBL schemes used in NOAA’s GFS model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6531, https://doi.org/10.5194/egusphere-egu22-6531, 2022.

The Observing Systems Simulation Experiment (OSSE) is a very powerful and widely applied approach to evaluate observing systems and data assimilation methods in numerical weather prediction (NWP). In the OSSE, we generate a nature run (NR) using a model and simulate observations by sampling the NR. An independent model run with data assimilation of the simulated observations mimics an NWP system, and we compare it with the NR to evaluate the observations and data assimilation method. In this study, we extend the OSSE and design the Control Simulation Experiment (CSE), in which we add perturbations to the NR and try to modify it to a desired state. Investigating what perturbations are effective to avoid a high-impact weather event would be useful to understand the controllability of such an event. Since the weather system is chaotic, and even more so for disturbances, small differences generally lead to big differences, particularly for high-impact weather events. This suggests potentially effective control, i.e., small interventions would lead to big differences for high-impact weather events. The chaos control has been studied extensively in the field of dynamical systems theory, but taking advantage of dynamical instability to avoid certain trajectories has not been a main focus to the best of the authors’ knowledge. We first tested this idea with the Lorenz-63 3-variable model and performed an OSSE with an ensemble Kalman filter (EnKF). We extended the OSSE by adding very small perturbations (only 3% of the observation error) to the NR and found an effective approach to control the trajectory to stay in one side of the Lorenz’s butterfly attractor without shifting to the other. Following the implications and understandings from the Lorenz-63 model experiments, we tested with the Lorenz-96 40-variable model to avoid the occurrences of extreme values, mimicking to avoid extreme events in NWP. Finally, we further extended the idea to test with realistic global and regional NWP models. This presentation will summarize the concept and methodology of CSE with some proof-of-concept demonstrations with the toy models and realistic NWP models. This is an attempt to a potential paradigm change of NWP research from decades of predictability to the new era of controllability.

How to cite: Miyoshi, T., Sun, Q., Terasaki, K., and Maejima, Y.: From Predictability to Controllability: Control Simulation Experiment (CSE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6707, https://doi.org/10.5194/egusphere-egu22-6707, 2022.

The earth’s atmosphere is a nonlinear and chaotic system. A small difference in the initial condition makes forecast different due to the chaos, the characteristics known as the “butterfly effect”. The predictability has been improved by the development of the NWP model, data assimilation, and observations for a long time. However, severe weather such as typhoon and torrential rainfall is a threat for us. Weather modification has also been investigated, such as cloud seeding and rain enhancement. It distributes cloud condensation nuclei and enhances cloud formation based on the microphysical processes. Alternatively, this study explores to control a typhoon by taking advantage of the chaotic dynamics.

The Observing System Simulation Experiment (OSSE) is a widely used approach in data assimilation research. We extend the OSSE to what we call the control simulation experiment (CSE) which changes the nature state to the desired direction by adding control signals determined by the ensemble forecasts. This study targets a typhoon, which generated over the Northwest Pacific and hit Japan. We perform CSEs to weaken the typhoon, i.e., making the central sea level pressure (SLP) higher. We apply the control only to the horizontal wind field at the first model vertical layer. Here, we limit the control signal only to reduce the kinetic energy because it would be difficult to increase kinetic energy in a real-world intervention. The CSE is found effective, i.e., we successfully weaken the typhoon when it reaches Japan. We will present the most recent results at the meeting.

How to cite: Terasaki, K. and Miyoshi, T.: Control simulation experiment for a typhoon case with a global numerical weather prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7089, https://doi.org/10.5194/egusphere-egu22-7089, 2022.

As the fidelity of global numerical weather prediction (NWP) models to resolve convective scale features increases with advances in computing power, high-resolution observations of clouds and precipitation are becoming increasingly important for both evaluating model performance and initialising forecasts. This talk focusses on the latter by presenting developments made to the ECMWF integrated forecast system (IFS) to allow the direct 4D-Var assimilation of spaceborne cloud radar and lidar observations. Due to the radar and lidar signal’s ability to penetrate clouds, these observations provide a unique insight to the vertical and horizontal structure of clouds. The additional information provides a fantastic opportunity to improve the model analysis of cloud and precipitation and the subsequent forecast, however extracting useful information from these observations, which are often only partially resolved by the model, pushes current data assimilation systems to their limit.

In this talk we will provide an overview of the developments to the IFS to allow the assimilation of cloud radar and lidar, including a triple-column technique to represent unresolved condensate variability in the simulated observations and the characterisation of observation error, both of which are vital to optimise the observations’ use. We will then give a thorough assessment of the impact of assimilating cloud radar and lidar on NWP forecast skill by assimilating CloudSat radar reflectivity and CALIPSO lidar backscatter on top of routinely assimilated observations. As well as showing improvements by evaluating forecasts against analyses, we will show the observations provide increases in forecast skill when verifying against independent observations, such as top-of-atmosphere radiative fluxes. Looking to the future, the upcoming ESA EarthCARE satellite mission will provide the opportunity to assimilate cloud radar and lidar observations operationally. Differences between CloudSat and CALIPSO with EarthCARE observations will be briefly discussed along with the potential for synergistic uses of other EarthCARE observations, such as Doppler velocity, cloud extinction and shortwave radiances.

How to cite: Fielding, M. and Janisková, M.: Improving NWP forecasts through the direct 4D-Var assimilation of space-borne cloud radar and lidar observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7441, https://doi.org/10.5194/egusphere-egu22-7441, 2022.

A semi-Lagrangian algorithm (SLA) is implemented in NOAA's Global Forecast System (GFS) for
simulating raindrop sedimentation in a double-moment microphysics schemes. This SLA includes
a significant improvement to its predecessor for single-moment raindrop sedimentation. It is
numerically stable and mass-conserving when used to sediment raindrops in double-moment
microphysics schemes. Numerical results from an idealized single-column model show that the
SLA overcomes an issue of mass accumulation at the cloud bottom in the case of the Eulerian
algorithm for raindrop sedimentation, which is due to the assumption of constant terminal
velocity within a time step of sedimentation. The results from the single-column model also show
that the time step in the SLA can be 10 times greater than that in the Eulerian algorithm for
sedimentation. Further numerical experiments using NOAA's GFS show that using the SLA
mitigates the numerical instability problem associated with a newly-implemented double-moment
microphysics scheme in the GFS.

How to cite: Hong, S., Li, H., Bao, J.-W., Grell, G., and Sun, R.: A Semi-Lagrangian Advection Algorithm for Falling Raindrops in aTwo-Moment Microphysics Schemes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8984, https://doi.org/10.5194/egusphere-egu22-8984, 2022.

As we enter the era of post peta-scale computing, convective-scale NWP will be performed at increasingly higher model resolutions, using more sophisticated data assimilation (DA) schemes and advanced observational datasets. Here we explore the implications for a regional-scale numerical weather prediction system that uses a unique 30-second update for a 500-m grid, using observations from an advanced multi-parameter phased array weather radar (MP-PAWR), on forecasts of convective weather systems. Experiments showed a rapid buildup in the level of atmospheric dynamical activity in the analyses from the start of cycling that promoted the initialization of spurious and often overly-strong convection in forecasts. This was found to be the consequence of substantial differences between the initial conditions and observations and the rapid updating process, which together introduced large perturbations to the analyses during early cycling, leading to the generation of an atmospheric state that was characterized by strong low-level winds and regions of high convective instability. These conditions would remain at a near constant level well after the period of initial cycling, continuing to be a strongly determining factor on the level of development of convection in the forecasts. It was subsequently demonstrated that we could reduce the level of convective activity in forecasts and so improve forecast skill by reducing the localization scale parameter to near model grid resolution, which acted to force initial conditions closer to the initial set of observations following the first update and reduce the large pertubations that caused these conditions to develop.

How to cite: taylor, J., Honda, T., Amemiya, A., otsuka, S., and Miyoshi, T.: Implications of a 30-second Update Cycle for a Convective-Scale Ensemble Radar Data Assimilation System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9244, https://doi.org/10.5194/egusphere-egu22-9244, 2022.

2021 was a standout year for ECMWF in that not one, but two major upgrades were made to the operational NWP system.

Cycle 47r2 (introduced on 11 May) increased the ensemble forecast (ENS) vertical resolution from 91 to 137 levels, bringing it into line with the high-resolution forecast (HRES). The cost of this, which is significant, was offset by running the forecast model in single precision which saved equivalent cost and is meteorologically neutral. Overall validation showed statistically significant skill improvements by the ENS forecasts, for many fields, mostly in the range 0.5-2% RMS error reduction. It also showed improvements for specific meteorological phenomena (e.g. Tropical Cyclones, Madden-Julien Oscillation).

Cycle 47r3 (introduced on 12 October) contained model, assimilation and observation usage changes. A major change, and the result of many years of research, was a complete new moist physics package. This brings significant meteorological benefit, and it this aspect users of ECMWF forecasts will see, but it also simplifies and modernizes the physics code in the IFS, and this will facilitate future improvements. This physics package includes too many changes to list here, but includes a more consistent formulation of boundary layer turbulence, shallow convection and sub-grid cloud and a new parametrized deep convection closure with an additional dependence on total advective moisture convergence. On the observation and data assimilation side the new weak constraint 4D-Var approach was applied in the Ensemble of Data Assimilations, and the all-sky observation assimilation approach was extended to a temperature sounder for the first time (AMSU-A), as well as a major update in the radiative transfer model for observation assimilation.

Cycle 47r3 validation showed significant improvements. For example, extratropical upper-air geopotential and wind in the first few days of the forecast improved by 1-2% and tropical upper-air winds throughout the medium-range improved by 1-4%. Also, tropical cyclone track errors have been reduced by 10%.

Cycle 47r3 is now being ported to the new ATOS HPC in the new ECMWF data centre in Bologna. Following the migration, the first science upgrade will be Cycle 48r1 and will contain some very important changes. The most important from a user perspective will be the ENS resolution change to TCo1279 (~9 km), hence matching the current HRES (which will remain unchanged). There will also be a large number of other changes, including the first use of the OOPS system for 4D-Var. OOPS is a modern code system that encapsulates tasks as objects, enabling both separation of concerns and more flexible interaction between components. The cycle will also see the introduction of a new multi-layer snow scheme (improving predictions of snow and of near-surface temperatures over snow), and enhancements to the use of satellite data over land. This last change represents a step on ECMWF’s strategic direction to get yet more value out of satellite data by moving from an ‘all-sky’ to an ‘all-sky, all-surface’ approach.

How to cite: Brown, A., Browne, P., English, S., Pappenberger, F., and Rabier, F.: Recent and planned NWP developments at ECMWF, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10235, https://doi.org/10.5194/egusphere-egu22-10235, 2022.

Data assimilation plays a critical role in the advancement of numerical weather prediction (NWP) via ingesting information of atmospheric observations from various platforms. As more and more observations become available, it is important to quantify the impact of assimilated observations on a forecast to help improve the use of these observations. Currently several approaches exist to estimate observational impact on the forecast skills. Ensemble Forecast Sensitivity to Observations (EFSO) is one such approach that extends upon the adjoint-based FSO method by utilizing ensemble of forecasts in replacement of an adjoint model. However, like any ensemble-based methods, EFSO also suffers from sampling error due to the use of limited-sized ensemble. This is more severe when we take ensemble-based correlations between different times. In recent years, the rapid advancement of supercomputing has facilitated the use of large number of ensemble members in NWP. Many studies have demonstrated the use of large ensembles in the context of data assimilation, however, the use of large ensemble to quantify observation impact via EFSO is yet to be explored. In this study, we implemented the EFSO method for a global atmospheric data assimilation system that consists of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) with the Local Ensemble Transform Kalman Filter (LETKF), namely the NICAM-LETKF. Using a total of 1024 ensemble members, we can examine the sensitivity of ensemble size to the accuracy of EFSO estimated error reduction via sub-sampling. We will present results from a series of EFSO experiments with the 1024-member NICAM-LETKF to conclude our findings.    

How to cite: Wu, T.-C., Terasaki, K., Kotsuki, S., and Miyoshi, T.: Examining the Sensitivity of the Accuracy of EFSO to Ensemble Size, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10704, https://doi.org/10.5194/egusphere-egu22-10704, 2022.

Motivated by the need to predict dust-storms, a large set of wind observations are compared to 24 h point forecasts with a high-resolution numerical model.  The cases are classified according to the dynamic nature of the winds; (a) wind over flat land, (b) enhanced winds blowing along a mountain (barrier/corner winds) and (c) downslope winds. The mean quality of the forecasts over flat land is similar to the quality of the forecasts of enhanced winds blowing along a mountain.  The quality of the forecast of the downslope winds is much poorer than the quality of the forecast of winds over flat land and winds blowing along a mountain.  In the downslope case, it is up to ten times more likely to either miss a windstorm or to forecast a windstorm that does not occur, than if the winds are not downslope.

How to cite: Ólafsson, H.: The connection between quality of wind forecasts and the dynamics of the winds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11169, https://doi.org/10.5194/egusphere-egu22-11169, 2022.

Temperature extremes are in general relatively difficult to forecast accurately and it is important to assess their nature and characteristics, both in numerical models and in reality.  Such extremes in Iceland have been explored and linked to two key parameters of the flow; low-level wind speed and static stability.  The results reveal very distinct distribution of cases in the space of these parameters:  Cold extremes in the winter occur only at low wind speeds, while in the summer, they occur only in low static stability.  Warm extremes in the winter occur on the other hand only at high static stability, and warm extremes in the summer occur only at low wind speeds.  This result can be summarized in The Couch Diagramme.

How to cite: Mollard, G. and Ólafsson, H.: Characteristics of extremely warm and extremely cold events in Iceland – The Couch Diagramme, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12001, https://doi.org/10.5194/egusphere-egu22-12001, 2022.

The German Weather Service (DWD) operationally runs an LETKF (Localized Ensemble Kalman Filter) assimilation scheme for the regional weather forecasts with the ICON-LAM (ICON Limited Area Mode) Numerical Weather Prediction model. We investigate the potential of using an EnVAR (Ensemble Variational data assimilation) using the kilometre-scale Ensemble Data Assimilation (KENDA) ensemble. Quality Control (QC) and Observation Aggregation (OA) are essential parts of a data assimilation system. The former ensures that the assimilated observations are likely to be "acceptable", in the sense of technical, physical and statistical properties. The latter reduces the amount of data and computations under the aspect of efficiency, and helps handling redundant or correlated observations.

We show results of assimilation experiments for KENDA and EnVAR using a similar selection of conventional observations after QC and OA, while using a fully dynamic B matrix and no variational QC. The difference of the results of the two algorithms does not only depend on the partially differing implementation of QC and OA, but also due to partially different implementations of the observation operators or even the supported observation types. Important differences to the operational global EnVAR code are e.g. the choice of suitable observation types and the interpolation specification of the first guess to the locations of the observations.

As we use the same code for the EnVAR as in the DWD's global data assimilation scheme, we can potentially assimilate many other observations systems beyond conventional observations. This includes, after some adaptations, a wide range of spaceborne observations. Additionally, it is possible to run a regional EnVAR assimilation and a deterministic forecast with a coarse resolution first guess ensemble. Re-using existing ensembles for the ensemble B matrix might be a computationally efficient way to use a variational algorithm for deterministic forecasts.

How to cite: Burba, M., Hollborn, S., Ulbrich, S., Schraff, C., Anlauf, H., Potthast, R., and Knippertz, P.: EnVAR Quality Control and Observation Aggregation for ICON-LAM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12132, https://doi.org/10.5194/egusphere-egu22-12132, 2022.

The WOD framework is a distributed system for:

• gathering upstream weather forecasts and observations from a wide variety of sources
• triggering scheduled or on-demand jobs
• running the WRF weather model for data-assimilation and forecasts
• processing data for long to medium-term storage
• making results available through APIs
• making data files available to custom post-processors

Much effort is put into starting processing as soon as the required data becomes available and in parallel when possible. The software is maintained in Git and can be installed on suitable hardware in a matter of hours, bringing the full flexibility and power of the WRF modelling system at your fingertips.

How to cite: Rögnvaldsson, Ó., Ragnarsson, L., and Stanisławska, K.: The WOD framework for weather forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12399, https://doi.org/10.5194/egusphere-egu22-12399, 2022.

The Common Community Physics Package (CCPP) is a collection of atmospheric physical
parameterizations and a framework that couples the physics for use in Earth system models.
The CCPP Framework was developed by the U.S. Developmental Testbed Center (DTC) and is
now an integral part of the Unified Forecast System (UFS), a community-based, coupled,
comprehensive Earth modeling system designed to support research and be the source system
for NOAA‘s multi-scale operational numerical weather prediction applications.

A primary goal for this effort is to facilitate research and development of physical
parameterizations, while simultaneously offering capabilities for use in operational models. The
CCPP Framework supports configurations ranging from process studies to operational
numerical weather prediction as it enables host models to assemble the parameterizations in
flexible suites. Framework capabilities include flexibility with respect to the order in which
schemes are called, ability to group parameterizations for calls in different parts of the host
model, and ability to call some parameterizations more often than others. Furthermore, the
CCPP is distributed with a single-column model that can be used to test innovations and to
conduct hierarchical studies in which physics and dynamics are decoupled.

There are today more than 30 primary parameterizations in the CCPP, representing a wide
range of meteorological and land-surface processes. Experimental versions of the CCPP also
contain chemical schemes, making it possible to create suites that tightly couple chemistry and
meteorology.

The CCPP is developed as open-source code and has received contributions from the wide
community in the form of new schemes and innovations within existing schemes. In this poster,
we will provide an update on CCPP development and plans, as well as review existing
resources for users and developers, such as the public releases, documentation, tutorial, and
forum

How to cite: Bernardet, L., Firl, G., Heinzeller, D., Zhang, M., Trahan, S., Dudhia, J., Kavulich, M., and Ek, M.: Facilitating the development of complex models with the Common Community Physics Package and its Single-Column Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13128, https://doi.org/10.5194/egusphere-egu22-13128, 2022.

Recent research, based on remote sensing of Normalized Difference Vegetation Index (NDVI) has revealed substantial interannual variability in the maximum vegetation in Iceland.  This variability is primarily related to temperature, but also to some extent to precipitation.  Most, if not all, operational numerical weather prediction models for that region do however use climatological values for vegetation with no interannual variability.

A preliminary investigation of temperature forecasts in the highlands of Iceland indicates that high NDVI correlates with positive bias of temperature forecasts.  This is presumably associated with the impact of increased vegetation on the Bowen ratio in sparsely vegetated regions, but local circulation may also play a role.  

How to cite: Rousta, I. and Olafsson, H.: Vegetation variability and temperature forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13283, https://doi.org/10.5194/egusphere-egu22-13283, 2022.

The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extreme SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

How to cite: Ngoungue Langue, C. G., Lavaysse, C., Vrac, M., Peyrillé, P., and Flamant, C.: Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13503, https://doi.org/10.5194/egusphere-egu22-13503, 2022.