AS1.1

Urban-weather forecasts are necessary for well-known applications such as air pollution and urban comfort predictions. In the past few years additional uses arose such as urban air traffic by drones and helicopters. All of these applications require high-resolution numerical weather-forecasts that need to include the effect of the urban canopy. While CFD and LES methods are necessary to provide detailed information about the flow at the street level, mesoscale forecasts are needed to provide their initial and boundary conditions.

This work presents very-high resolution (500-m grid size) WRF simulations over a coastal complex terrain metropolitan area, Haifa, Israel, which is prone to high pollution events.

The simulations include three approaches to simulate the impact of the city on the simulated urban weather:

• Bulk parameterization; which corresponds to the default MODIS landuse categories of the WRF modeling system.
• Detailed local urban-canopy information for the Haifa metropolitan area derived with the help of a GIS tools was used with the two following urban canopy modules:
• The single-layer urban canopy (SLUCM) parametrization.
• The multi-level layer urban- canopy parameterization, specifically the building-effect parameterization with building energy model (BEP-BEM).

We focused on a wide variety of synoptic-scale weather conditions that, among others, can lead to or worsen high pollution events. The simulations used ERA5 reanalyses for initial and boundary conditions. We explored the sensitivity of the simulated urban flow and heat island effect to the planetary boundary layer parameterizations (YSU and Boulac), and the urban canopy modeling. Due to the lack of specific anthropogenic-heat information for the Haifa area, we used crude estimations of the timing and desired temperatures for air-conditioning usage in the BEP-BEM parameterization, and a typical diurnal cycle of anthropogenic heat for the SLUCM parameterization (with estimation of the maximal heat loads following literature for cities in similar climate zones and with similar population).

The simulations were compared to near surface observations of wind, temperature and relative humidity within and outside the urban area, and to vertical soundings at the only launching location in Israel, Beit-Dagan. Objective verification scores as well as visual verification of 2D maps of the aforementioned variables demonstrate that the simulations reproduce the different mesoscale dynamics under very different synoptic conditions. The impact of the detailed urban modeling (BEP-BEM and SLUCM) without specific information on the anthropogenic-heat, is limited in this case.  

How to cite: Rostkier-Edelstein, D., Berkovic, S., Chudnovsky, A., and Avisar, D.: Very-high resolution WRF mesoscale urban-modeling for a coastal complex terrain metropolitan area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2424, https://doi.org/10.5194/egusphere-egu22-2424, 2022.

Torrential rainfall is a threat in the modern society. To predict severe weather, convection resolving numerical weather prediction (NWP) is effective. This study explores a Control Simulation Experiment (CSE) aimed at controlling precipitation amount and locations to potentially prevent catastorphic disasters by simulating different scenarios of interventions of small perturbations taking advantage of the chaotic nature of dynamics. In this study, we perform a CSE using a regional model SCALE-RM for a severe rainfall event which caused catastrophic landslides and 77 fatalities in Hiroshima, Japan on August 19 and 20, 2014.

We perform a 1-km-mesh, hourly-update, 50-member observing system simulation experiment (OSSE) for this rainfall event initialized at 0000 UTC August 18. This provides the initial conditions for a 6-hour ensemble forecast initilaized at 1500 UTC Augest 19. To create small perturbations to change the nature run, we take the differences of all model variables between an ensemble member having the heaviest rain and another ensemble member having the weakest rain. Moreover, we normalize the perturbations so that the maximum wind speed is 0.1 m s-1. In this preliminary CSE, we try to control the heavy rainfall by giving the perturbations to the nature run in the OSSE at each time step from 1500 UTC to 1600 UTC on August 19, although the perturbations for all variables at all grid points are something beyond human’s engineering capability. In the nature run, 6-hour accumulated rainfall amount from 1500 UTC to 2100 UTC reaches 210 mm at the peak grid point. By contrast, the rainfall amount decreases to 118 mm in the CSE. We plan to apply limitations to the perturbations.

How to cite: Maejima, Y. and Miyoshi, T.: A Control Simulation Experiment for August 2014 Severe Rainfall Event Using a Regional Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3269, https://doi.org/10.5194/egusphere-egu22-3269, 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 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.

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 Grell-Freitas (GF) cumulus parameterization is an aerosol-aware, scale-aware convective parameterization that has been used globally and regionally. This presentation will focus on one of the several developmental activities ongoing in GF: the continued development of its aerosol-aware capabilities and the impact on global forecast models. While it is well established that aerosols impact weather and climate, relatively little work has been done to represent their impact in medium-range forecasts and in convective parameterizations.

GF includes three aerosol related cloud processes: aerosol-influenced auto-conversion of cloud water to rain water, aerosol dependent precipitation efficiency, and aerosol wet scavenging based on memory and precipitation efficiency. Additionally, if aerosols are based on analysis or climatologies, they are allowed to slowly return to their original concentrations during precipitation-free periods.

In its most simplistic approach, aerosol pollution in GF is characterized using aerosol-optical depth (AOD). The method of our application is extremely efficient and can be adapted to use different aerosol or AOD products.  For example, other products that could be used include the aerosol climatology used by the Thompson Aerosol-Aware Microphysical Parameterization or predicted aerosols using NOAA’s aerosol prediction model, which is currently one ensemble in the Global Ensemble Forecast System – Aerosols (GEFS-Aerosols). The treatment of aerosols in GF should be most sensitive in regions with either very high or very low levels of pollution.

The impact of these changes to GF will be shown in a version of NOAA’s experimental global prediction model, with 

How to cite: Barnes, H., Grell, G., and Freitas, S.: Aerosol impacts for convective parameterizations: Applications of the Grell-Freitas Convective Parameterization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6450, https://doi.org/10.5194/egusphere-egu22-6450, 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.

In order to conduct network design experiments for a forecast system, methods are needed to evaluate the potential benefit of hypothetical observations. Ideally these methods are flexible enough to accommodate multiple observation types and forecast lead times, while being computationally fast enough to evaluate many potential observational network layouts. For ensemble forecasts, this can be achieved by assuming a linear sensitivity between the background ensemble perturbations and a forecast quantity of choice. This assumption enables estimating how much the ensemble variance of a chosen forecast quantity would be reduced for an arbitrary combination of observation locations and types, without the need to run additional forecasts. These variance reduction estimates need to take the localization used in the data assimilation framework into account, so that the estimates are consistent with the ensemble forecast system they are derived for. This localization aspect has so far received little attention.

In this presentation we compare two methods to take localization into account when estimating the benefit of hypothetical observations. One method requires inverting the background ensemble covariance matrix. The other method avoids the inversion, but needs to be provided with estimates of signal propagation over time. We use a simple linear advection toymodel to show that while both methods can function well, due to their various strengths and weaknesses they are suited to different applications.

How to cite: Griewank, P., Löhnert, U., Necker, T., Nomokonova, T., and Weissmann, M.: Accounting for localization in ensemble network design experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7393, https://doi.org/10.5194/egusphere-egu22-7393, 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.

The ensemble data assimilation (EDA) system contains uncertainties both in initial conditions and model forecast. In general, the uncertainties are represented by the ensemble spread that is a standard deviation of background error covariance (BEC). However, this ensemble spread is usually underestimated due to insufficient ensemble size, sampling errors, and imperfect models: it often causes a filter divergence problem as the analysis ignores the observation due to insufficient model uncertainty. This phenomenon is also found in the coupled land-atmospheric modeling system, especially near the surface where the heat flux exchanges are crucial as the lower boundary conditions. Therefore, we have developed the stochastically perturbed parameterization (SPP) scheme for the Noah Land Surface Model (hereafter, SPP-Noah LSM) using the soil temperature and moisture within the coupled WRF-Noah LSM system to represent the near-surface uncertainty. It perturbs the soil variables by adding the random forcing to inflate the ensemble spread. In particular, the random forcing used in perturbation is controlled by the tuning parameters such as amplitude, time scale, and length scale, which vary depending on the target variables. To obtain the optimal random forcing parameters to the soil variables, we employed a global optimization algorithm --- the micro-genetic algorithm, which is based on the natural selection or survival of fitness to evolve the best potential solution. The optimization is conducted in each daytime and nighttime to consider the diurnal variations of soil variables. As a result, the soil temperature and moisture perturbations using the SPP-Noah LSM can indirectly inflate the ensemble BECs of temperature and water vapor mixing ratio through the heat flux changes, respectively, in the planetary boundary layer (PBL) of the EDA system. The SPP-Noah LSM with diurnal variations depicts reasonable ensemble spreads for soil variables, but the ensemble spreads for atmospheric variables from the propagation of the soil variable perturbations are less effective. Our results indicate that the inflated ensemble spread helps to produce an adequate analysis increment reducing the background error in the PBL.

How to cite: Lim, S., Park, S. K., and Cassardo, C.: Optimized Stochastically Perturbed Parameterization Scheme for the Soil Temperature and Moisture within an Ensemble Data Assimilation System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10818, https://doi.org/10.5194/egusphere-egu22-10818, 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.

This study investigates the medium-range predictability of persistent warm and cold extremes in Europe. To that end, deterministic ERA5 reforecasts for the period 1979-2019 are compared to the reanalysis of the respective period, thus providing a large sample for verification and bias identification. The seasonally-varying Gilbert skill score of both extreme event types reveals that cold extremes in summer exhibit particularly low predictability. A spatial variability also emerges for these scores with persistent extremes in northeastern Europe and Scandinavia generally achieving better predictability compared to other regions of Europe. Composites of basic reanalysis fields and their errors suggest that the aforementioned spatiotemporal variability in predictability is associated with differences in the typical synoptic conditions of each type of event. Moreover, it is shown that summer and winter in Europe suffer from a negative and positive bias in Rossby wave amplitude, respectively. Although the physical processes and model deficiencies involved are not straightforward to identify, we hypothesize that these biases constitute one of the factors that limit the predictability of temperature extremes at weather time scales.

How to cite: Fragkoulidis, G., Doensen, O., and Wirth, V.: Predictability of temperature extremes in Europe and biases in Rossby wave amplitude, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11665, https://doi.org/10.5194/egusphere-egu22-11665, 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.

In this study, we used the Weather Research and Forecasting (WRF) version 4.2.1 model to simulate the characteristics of a rainfall-induced landslide that occurred on November 28 in Samigaluh, Kulonprogo. In addition, we investigated 22 different microphysics (MP) schemes to see how sensitive they were. The WRF model was employed with three nested domains, the innermost of which had a 1 km grid spacing and explicit convection. The model was run for 73 hours with GFS initial conditions from 00:00 UTC on November 26, 2018. We used reflectivity profiles from the Weather Radar in Yogyakarta and data from rain gauge stations in Kulonprogo to validate the simulated properties of the rainfall. Despite employing identical initial and boundary conditions and model settings, the MP approaches have significant variances in their thunderstorm simulations. To begin with, practically all of the extreme convection simulation methods over Samigaluh had the same pattern as the reported storm. For example, on November 27, radar data indicated the passage of three convective cores above Samigaluh, which the model in most MP schemes simulated. In comparison, the Ferrier_old scheme did an excellent job of simulating the convective cores' observable features. The MP schemes also had difficulties modeling the storm's updrafts. The Ferrier old scheme simulated surface rainfall distributions closer to data than the other three schemes (Goddard GCE, Morrison2, and WDM5). On the other hand, all four MP systems did an excellent job of simulating the convective variations associated with the thunderstorm. The model's generated reflectivity profiles, which showed three convective cores, were similar to the observed reflectivity profile. These characteristics match the simulated convective profiles, which peaked between 10 and 15 kilometers. The current research reveals that the microphysical systems in thunderstorm simulations have a lot of sensitivity and variability. The study also underlines the necessity for a multi-observational program such as Year of Maritime Continent (YMC) to improve the parameterization of cloud microphysics and land surface processes throughout the Indonesian region. 

How to cite: Nuryanto, D. E., Satyaningsih, R., Nuraini, T. A., Sopaheluwakan, A., Ettema, J., Jetten, V. G., Permana, D. S., Riama, N. F., and Karnawati, D.: Simulation of a heavy rainfall-induced landslide event over Kulonprogo, Yogyakarta in Indonesia using WRF: Sensitivity to cloud microphysics parameterization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12675, https://doi.org/10.5194/egusphere-egu22-12675, 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.