Sensitivity study of the REMO regional climate model to domain size

In the framework of the KlimAdat national project, the Hungarian Meteorological Service (OMSZ) is aiming to perform 10 km horizontal resolution simulations with the 2015 version of the REMO regional climate model over Central and Eastern Europe. The long-term simulations were preceded by a 10-year long sensitivity study on domain size, which is summarised in this paper. We selected three different domains embedded in each other, which contain the whole area of the Danube and Tisza river catchments. Lateral boundary conditions were obtained from the 50 km resolution REMO driven by the MPI-ESM-LR global climate model. Simulations were performed for the period of 1970–1980 including 1-year spin-up. Monthly and seasonal means of daily 2 m temperature, precipitation sum and several precipitation indices were evaluated. Reference datasets were E-OBS 19.0 and CarpatClim-HU. We can conclude, that the selection of domain size has a larger impact on the simulation of precipitation, and in the case of the seasonal mean of the precipitation indices, the differences amongst the results obtained on each model domain exceed 10 %. In general, the smallest biases occurred on the largest domain, therefore further long-term simulations are being produced on this domain.

Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools

Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.

Towards a reproducible snow load map – an example for Austria

The European Committee for Standardization defines zonings and calculation criteria for different European regions to assign snow loads for structural design. In the Alpine region these defaults are quite coarse; countries therefore use their own products, and inconsistencies at national borders are a common problem. A new methodology to derive a snow load map for Austria is presented, which is reproducible and could be used across borders. It is based on (i) modeling snow loads with the specially developed Δsnow model at 897 sophistically quality controlled snow depth series in Austria and neighboring countries and (ii) a generalized additive model where covariates and their combinations are represented by penalized regression splines, fitted to series of yearly snow load maxima derived in the first step. This results in spatially modeled snow load extremes. The new approach outperforms a standard smooth model and is much more accurate than the currently used Austrian snow load map when compared to the RMSE of the 50-year snow load return values through a cross-validation procedure. No zoning is necessary, and the new map's RMSE of station-wise estimated 50-year generalized extreme value (GEV) return levels gradually rises to 2.2 kN m−2 at an elevation of 2000 m. The bias is 0.18 kN m−2 and positive across all elevations. When restricting the range of validity of the new map to 2000 m elevation, negative bias values that significantly underestimate 50-year snow loads at a very small number of stations are the only objective problem that has to be solved before the new map can be proposed as a successor of the current Austrian snow load map.

Winter Subseasonal Wind Speed Forecasts for Finland from ECMWF

The subseasonal forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts) were used to construct weekly mean wind speed forecasts for the spatially aggregated area in Finland. Reforecasts for the winters (November, December and January) of 2016–2017 and 2017–2018 were analysed. The ERA-Interim reanalysis was used as observations and climatological forecasts. We evaluated two types of forecasts, the deterministic forecasts and the probabilistic forecasts. Non-homogeneous Gaussian regression was used to bias-adjust both types of forecasts. The forecasts proved to be skilful until the third week, but the longest skilful lead time depends on the reference data sets and the verification scores used.

Evaluation of wind speed estimates in reanalyses for wind energy applications

A correct spatio-temporal representation of retrospective wind speed estimates is of large interest for the wind energy sector. In this respect, reanalyses provide an invaluable source of information. However, the quality of the various reanalysis estimates for wind speed are difficult to assess. Therefore, this study compares wind measurements at hub heights from 14 locations in Central Europe with two global (ERA5, MERRA-2) and one regional reanalysis (COSMO-REA6). Employing metrics such as bias, RMSE and correlation, we evaluate the performance of the reanalyses with respect to (a) the local surface characteristics (offshore, flat onshore, hilly onshore), (b) various height levels (60 to 200 m) and (c) the diurnal cycle. As expected, we find that the reanalyses show the smallest errors to observations at offshore sites. Over land, MERRA-2 generally overestimates wind speeds, while COSMO-REA6 and ERA5 represent the average wind speed more realistically. At sites with flat terrain, ERA5 correlates better with observations than COSMO-REA6. In contrast, COSMO-REA6 performs slightly better over hilly terrain, which can be explained by the higher horizontal resolution. In terms of diurnal variation, ERA5 outperforms both other reanalyses. While the overestimation of MERRA-2 is consistent throughout the day, COSMO-REA6 significantly underestimates wind speed at night over flat and hilly terrain due to a misrepresentation of nightly low level jets and mountain and valley breezes. Regarding the representation of downtime of wind turbines due to low/high wind speeds, we find that MERRA-2 is consistently underperforming with respect to the other reanalyses. Here COSMO-REA6 performs better over the ocean, while ERA5 shows the best results over land.

Climate change projections of maximum temperature in the pre-monsoon season in Bangladesh using statistical downscaling of global climate models

The climate of Bangladesh is very likely to be influenced by global climate change. To quantify the influence on the climate of Bangladesh, Global Climate Models were downscaled statistically to produce future climate projections of maximum temperature during the pre-monsoon season (March–May) for the 21st century for Bangladesh. The future climate projections are generated based on three emission scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by the fifth Coupled Model Intercomparison Project. The downscaling process is undertaken by relating the large-scale seasonal mean temperature, taken from the ERA5 reanalysis data set, to the leading principal components of the observed maximum temperature at stations under Bangladesh Meteorological Department in Bangladesh, and applying the relationship to the GCM ensemble. The in-situ temperature data has only recently been digitised, and this is the first time they have been used in statistical downscaling of local climate projections for Bangladesh. This analysis also provides an evaluation of the local data, and the local temperatures in Bangladesh show a close match with the ERA5 reanalysis. Compared to the reference period of 1981–2010, the projected maximum pre-monsoon temperature in Bangladesh indicate an increase by 0.7/0.7/0.7 ∘C in the near future (2021–2050) and 2.2/1.2/0.8 ∘C in the far future (2071–2100) assuming the RCP8.5/RCP4.5/RCP2.6 scenario, respectively.

Full flowering phenology of apple tree (<i>Malus domestica</i>) in Pūre orchard, Latvia from 1959 to 2019

The Pūre orchard is one of the oldest apple orchards in the Baltic, where thousands of varieties of fruit trees from throughout the world are grown and tested. Over time, a huge knowledge base has been accumulated, but most of the observational data are stored in archives in paper format. We have digitized a small part of the full flowering phenological data of apple trees (Malus domestica) over the period of 1959 to 2019 for 17 varieties of apple trees, a significant step for horticulture and agricultural economics in Latvia. Climate change has led to significant changes in the phenology of apple trees as all varieties, autumn, summer and winter, have begun to flower earlier: from 2002 to 2019, on average full flowering was recorded to have taken place around 21 May, whereas for the period 1959–1967 it occurred around 27–28 May. To develop better-quality phenological predictions and to take account of the fragmentary nature of phenological data, in our study we assessed the performance of three meteorological data sets – gridded observation data from E-OBS, ERA5-Land reanalysis data and direct observations from a distant meteorological station – in simple phenological degree-day models. In the first approximation, the gridded E-OBS data set performs best in our phenological model.

LES study of precipitation/condensation dependance on cumulus clouds dynamics

Parameters affecting condensation/evaporation rates (CR/ER) in trade wind cumulus clouds were analyzed using LES model simulations. The model was initialized with data observed during the RICO field project, and simulated in a rather large 50.0×50.0×4 km3 domain. 2031 clouds were analyzed seeking relationships between CR/ER and thermo-dynamical cloud parameters. The condensation/evaporation rates were analyzed by stratifying the clouds by their size. The analyzed parameters included, among others, integral mass and buoyancy fluxes, as well as cloud and rain water and drop concentration. The results revealed rather remarkable relationship between integral condensation/evaporation rate and integral upward mass flux. Identified relathionship may be useful for parameterization of subgrid latent heat in meso and large-scale models.

Using machine learning to produce a very high resolution land-cover map for Ireland

Land-cover classifications in the form of maps are required for numerical modelling of weather and climate. Such maps are often of coarse resolution and are infrequently updated. Here we propose a novel approach for land-cover classification using a Convolutional Neural Network machine learning algorithm to segment satellite images into various land-cover classes. Sentinel-2 satellite imagery, the CORINE land-cover database and the BigEarthNet dataset are used. A 10 m resolution map, called the Ulmas-Walsh map, has been created for Ireland that outperforms ECO-SG in terms of accuracy, as well as demonstrating a capacity for identifying features not labelled correctly in CORINE. The map can be updated on demand for any time of the year, subject to cloud cover. This is particularly useful for regions with large seasonal variation in land classifications such as Turloughs – seasonal lakes, flood plains and rotational crops.

Visualization of radar-observed rainfall for hydrological risk assessment

Short-duration high-intensity rainfall constitutes a major hydro-meteorological hazard, with impacts such as pluvial (urban) flooding and debris flow. There is a great demand in society for improved information on small-scale rainfall extremes, both in real time (e.g. for early warning) and historically (e.g. for post-flood analysis). Observing this type of events is notoriously difficult, because of their extreme small-scale space-time variability. However, owing to recent advances in weather radar technology as well as integration with ground-based sensors, observational products potentially applicable in this context are now available. In this paper we present a visualization prototype tailored for hydrological risk assessment by using sub-basins as spatial units, by allowing temporal aggregation over different durations (i.e. accumulation periods) and by expressing high rainfall intensities in terms of return period exceedance. The radar-based data is evaluated by comparison with gauge observations and the quality is deemed sufficient for the intended applications. Different stakeholders have shown great interest in the prototype, which is openly accessible online.