High-Resolution Dynamical Downscaling of ERA-Interim Using the WRF Regional Climate Model for the Area of Poland. Part 2: Model Performance with Respect to Automatically Derived Circulation Types
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This paper presents the application of the high-resolution WRF model data for the automatic classification of the atmospheric circulation types and the evaluation of the model results for daily rainfall and air temperatures. The WRF model evaluation is performed by comparison with measurements and gridded data (E-OBS). The study is focused on the area of Poland and covers the 1981–2010 period, for which the WRF model has been run using three nested domains with spatial resolution of 45 km × 45 km, 15 km × 15 km and 5 km × 5 km. For the model evaluation, we have used the data from the innermost domain, and data from the second domain were used for circulation typology. According to the circulation type analysis, the anticyclonic types (AAD and AAW) are the most frequent. The WRF model is able to reproduce the daily air temperatures and the error statistics are better, compared with the interpolation-based gridded dataset. The high-resolution WRF model shows a higher spatial variability of both air temperature and rainfall, compared with the E-OBS dataset. For the rainfall, the WRF model, in general, overestimates the measured values. The model performance shows a seasonal pattern and is also dependent on the atmospheric circulation type, especially for daily rainfall.
KeywordsAtmospheric circulation rainfall air temperature WRF dynamical downscaling ERA-Interim circulation types
Spatial meteorological information is a key element in various environmental studies, including air pollution, hydrology (Jeziorska and Niedzielski 2015, this issue) and wind energy production (Badger et al. 2014; Mendez et al. 2014). This information can be provided in various ways, including GIS-based interpolation (Szymanowski et al. 2013) and statistical or dynamical downscaling (Giorgi and Bates 1989; Lo et al. 2008; Czernecki 2013). There is also a combined approach, named statistical–dynamical downscaling, which has also gained importance in climate research in recent years. Statistical–dynamical downscaling combines the benefit of both techniques and was presented, e.g. by Fuentes and Heimann (2000) and Reyers et al. (2015). The performance of these approaches is also evaluated in different ways, by comparing the results with available measurements and with other reference spatial data, including gridded information.
Atmospheric circulation plays a major role in daily, seasonal and spatial distribution of weather-related parameters, including air temperature and rainfall. Poland (Central Europe) is notable for transitional characteristics of climate, from maritime in the west to more continental in the east, and this region is the focus of the current study. There are many studies that link the local meteorological features, usually based on station measurements, with large-scale circulation patterns, classified into different groups using various approaches. For example, Osuchowska-Klein (1992) and Niedźwiedź (1981) analysed the relation between the air temperature and atmospheric circulation using a classification based on sea level pressure, while other authors proved that the spatial variability of air temperature and precipitation is highly correlated with the geopotential height at upper isobaric levels (Wibig 1991, 2001; Kożuchowski et al. 1992). Ustrnul (2000) and Ustrnul et al. ( 2010 ) have shown that a circulation type with anticyclonic ridge forms the most favourable conditions for the high air temperatures in summer. For winter, anticyclonic types with easterly flows are favourable for extremely low air temperatures. Synoptic conditions favourable to frosty, freezing and severe freezing days for Poland were analysed by Bielec-Bąkowska and Łupikasza (2009) and Ustrnul et al. (2014). Bednorz (2012) and Bednorz and Wibig (2008) showed that the atmospheric circulation has a large impact on intense thaws and snow conditions. Łupikasza (2010) analysed the relationship between atmospheric circulation and high daily precipitation in Poland, using various methods of classifications of circulation types. Niedźwiedź (1981), Twardosz and Niedźwiedź (2001) and Twardosz et al. (2011) analysed the role of synoptic circulation patterns on daily rainfall in south-west Poland for a long period and found that the advection of air masses from the west and cyclonic troughs are the most favourable to precipitation events. Finally, the circulation patterns were used during the construction of the GIS-based maps of meteorological elements spatial variability (Ustrnul 2006; Ojrzyńska 2015).
A detailed weather and climate analysis based on various data sets should be supported by the analysis of atmospheric circulation. There are different approaches to the classification of circulation types, and several of these methods were applied for the study area addressed in this paper (e.g. Litynski 1969; Niedźwiedź 1981; Osuchowska-Klein 1978; Gerstengarbe et al. 1993; Ustrnul 1997; Wibig 2001; Huth et al. 2008; Piotrowski 2009; Woyciechowska and Ustrnul 2011; Bednorz 2012). The results of the 733 COST action Harmonisation and applications of weather type classification for European Regions (cost733.met.no/FinalEvent.html) emphasize that high spatial variability of atmospheric circulation patterns preclude a general and universal method for circulation type classification and justifies the development and application of regional methodology. A long time series of meteorological data provides the opportunity to apply complex classification schemes with large numbers of distinct types. This kind of classifications could be troublesome in statistical analysis and practical applications, but also more circulation types allow a more detailed weather description in case studies. In this study, we develop an approach similar to the Objective Weather Type Classification (Die objective Wetterlagenklassifikation) of the German Weather Service (Bissolli and Dittmann 2001), which, apart from e.g. cyclonality and direction of the air masses advection, utilizes information on the humidity of the air. A modification of the original classification scheme described by Bissolli and Dittmann ( 2001 ) was previously successfully applied for the Sudetes Mountains (SW Poland) and their foreland (Ojrzyńska 2015), using coarse resolution gridded meteorological data. Here, the objective classification scheme is fed with the WRF-derived meteorological information, available at high spatial resolution. This is a novelty, as previous studies used coarse resolution meteorological information for classification of circulation types, coming from global reanalysis databases like NCEP or ERA (Ustrnul 1997; Bissolli and Dittmann 2001; Piotrowski 2009; Woyciechowska and Ustrnul 2011; Ojrzyńska 2015).
In the first part of this twin paper (Kryza et al. 2016), we have evaluated the results of the ERA-Interim dynamical downscaling with the Weather Research and Forecasting (WRF) model for the period 1981–2010 using 3-hourly measurements of air temperature, humidity and wind speed and direction. Here, the focus is on evaluation of daily rainfall and air temperature for the same period, with the context of synoptic situation. Earlier reports by Jiménez et al. (2013) suggest that the model performance might rely on the synoptic situation, and their analysis was focused on wind condition for complex terrain.
In this work, we first describe and apply a method for automatic classification of the circulation types (ACCT) in Poland and then apply the method to the extended period of 1981–2010 with WRF model data. Secondly, we compare the WRF model for daily rainfall and air temperature with respect to the circulation types derived by application of the ACCT method. The analysis of the regional dynamical downscaling with the WRF model is complemented by comparison with spatial data obtained through a geostatistical (spatial interpolation) method. This spatial comparison is also performed with respect to the circulation type, to assess if the model performance changes with the atmospheric circulation pattern.
2 Data and Methods
2.1 The WRF Model
The details of the WRF model (Skamarock et al. 2008) configuration are provided in the first part of this twin paper (Kryza et al. 2016). Here, we only provide some important remarks on the model configuration. The model has been run for a 30-year long period of 1981–2010, using ERA-Interim data as initial and boundary conditions. The model was applied for three one-way nested domains: d01 (45 km × 45 km grid for Europe), d02 (15 km × 15 km grid for Central Europe) and d03 (5 km × 5 km grid for Poland). In this study, we compare the measurements with the results from the innermost domain (d03) covering Poland with a 5 km × 5 km grid resolution, and the d02 results are used for classification of the circulation types. The model results are available for every 3 h, and the daily mean air temperature is calculated by averaging all time frames available for a given day. The WRF model rainfall is available for every 3 h as accumulated precipitation and was recalculated into daily sums. The same time spans (6 UTC–6 UTC) were used both for the model data and the measurements.
2.2 Calculation of Circulation Types
2.2.1 Direction of Advection
The direction of advection is calculated directly from the u and v wind components, provided by the WRF model for 700 hPa isobaric level. If the wind speed exceeds 2 m s−1, the wind direction for the analysed grid cell is assigned, using four main directions: NE, NW, SE, SW. Otherwise, if the wind speed is below the threshold value, the XX class is assigned to the grid cell. The dominant wind direction for the entire domain is the one that occurs for more than 50 % of the grid cells in this domain. If there is no prevailing wind direction for the area, the final type of criterion “direction of advection” is classified as XX.
2.2.2 Cyclonality for 825 (Lower) and 500 (Upper) Isobaric Level
Cyclonality is calculated as an approximated value of ∇2ϕ, where ∇ is the nabla operator and ϕ is a value of the geopotential. Cyclonality is calculated separately for the 500 and 825 hPa isobaric levels. A positive value of ∇2ϕ is classified for the cyclonic type (C), and negative for the anticyclonic (A) type. The calculation of grid cell cyclonality is a two-step procedure, based on the 3 × 3 grid neighbourhood. In the first step, every grid cell from a given neighbourhood is multiplied by value 1, while the analysed grid cell (centre of a given 3 × 3 neighbourhood) is multiplied by −8. In the second step, the mean value from all nine grid cells is calculated. The result is attributed to the analysed grid as an approximate value of ∇2ϕ. The calculation of ∇2ϕ is preceded by the generalization of the geopotential, which is averaged using a low-pass filter with size 3 × 3 grid cells.
2.2.3 Humidity Type
The humidity type for each grid cells is calculated as a result of subtracting the daily mean value of tropospheric precipitable water (PW) and the suitable areal long-term monthly mean of PW. A positive value of the difference is classified for wet (W) type and negative for dry type (D). The long-term mean PW is calculated using the WRF model data for the d02 domain.
An automatic tool for the circulation type classification took a form of script, prepared using the NCAR Command Language (NCL Software, Version 6.1.2, 2013). The script reads sequential netCDF files, which contain the outputs from the WRF model. The above-mentioned meteorological information, needed to determine the circulation type, is calculated for each grid cell and classified according to the classification criteria. The atmospheric circulation type is determined by the combination of the four classification criteria described above (direction of advection, lower and upper cyclonality and humidity type). This method allows for easy grouping of the detailed classification and reduction of particular classification criteria. This characteristic was utilized for the results of this study. The algorithm is flexible in terms of, e.g. the incorporation of additional meteorological parameters and can be applied for other areas and the WRF model configuration (e.g. in terms of spatial extent and grid resolution).
2.3 Meteorological Data for the WRF Model Evaluation
In this work, first we compare the 5 km × 5 km WRF model results with the meteorological measurements of daily rainfall and daily mean air temperature, gathered at 66 synoptic stations in Poland for the 1981–2010 period. For comparison, we used the WRF model domain d03 data from a grid cell, in which the measuring site is located. It should be noted here that we used the area averages (WRF model grid cell) and point values (measuring sites) in this work. The spatial distribution of the measurement sites is presented in the first part of this twin paper (please see Fig. 1 at Kryza et al. 2016). Secondly, we compare the WRF model results with the gridded meteorological information available for Europe and described by Haylock et al. (2008).
The European land-only daily high-resolution gridded data (E-OBS; Haylock et al. 2008) for daily rainfall and daily mean air temperature are available for all Europe with 0.25° × 0.25° spatial resolution. This dataset was developed by three-step spatial interpolation. First, the monthly mean values are interpolated with thin-plate splines. Second, the anomalies with regard to the monthly mean are interpolated using the kriging algorithm. The final map is calculated by applying the interpolated anomaly to the interpolated monthly mean (Haylock et al. 2008). In this work we used E-OBS data version 10.0.
2.4 The WRF Model Evaluation
Mean error (ME) calculated as an arithmetic mean from the model error for the air temperature in °C. A positive value of ME suggests a tendency for overestimation, while a negative value suggests underestimation of the air temperature by the model. For the rainfall, ME is given in percent, with the values >100 % showing overestimation of rainfall and <100 % showing underestimation.
Mean absolute error (MAE) calculated as the mean value of the absolute model error. The units are °C for the air temperature and mm/day for rainfall.
Index of agreement (IOA) calculated using the formula proposed by Emery et al. (2001), as a standardized measure of the degree of model prediction error. Details for IOA are provided in part 1 of this paper. The values vary between 0 (no agreement) and 1 (perfect match). IOA is unitless.
The error statistics are calculated using the data from all available stations and for the entire study period, each month and for each determined groups of circulation types. Histograms and the quantile–quantile plots are provided both for the WRF and E-OBS comparison with the measurements. The WRF and E-OBS are also compared spatially for long-term periods. Additionally, the IOA statistic was calculated for each grid separately for selected groups of atmospheric circulation types to give an insight into spatial differences between the WRF and the E-OBS datasets.
Similar to the approach used for the WRF model evaluation, the rainfall and air temperature values from E-OBS datasets were extracted for the grid in which the meteorological stations were located. It must be emphasised that the E-OBS database is based on the same measurements that are used here to evaluate the results of the WRF model and, therefore, should show very similar values as the measurements. However, because of the relatively coarse grid of 0.25°, there might be some issues related with spatial averaging and reduction of the extremes, which is one of the key points of this work. This includes, among others, averaging of the extreme values, both for air temperature and rainfall (Wibig et al. 2014). Additionally, for rainfall, the coarse resolution of the E-OBS database makes it less prone to incorrect spatial allocation of rainfall. The quantification of the differences between the E-OBS data and the measurements is important, because the E-OBS is used later in this work for the WRF model spatial evaluation.
The spatial distribution of the long-term mean values of rainfall and air temperatures was calculated with the WRF and E-OBS data and compared, using the original spatial resolutions of both datasets. This was done to show the value added by the higher spatial resolution of the WRF model. Secondly, we aggregated the WRF model data to the coarser E-OBS grid, and for each common grid cell we calculated the index of agreement and mean error statistics. This was possible, because for the entire period and each grid cell, time series of the WRF and E-OBS meteorological information was available. The IOA and ME were calculated for selected types of atmospheric circulation and presented as maps.
The results are organized as follows. First, the general comparison of the WRF and E-OBS data with measurements is presented, including spatial comparison of the WRF and E-OBS data. Both datasets are also compared spatially, using the source spatial resolutions of WRF and E-OBS. Secondly, the information on the circulation types and frequency is provided. Finally, the WRF model and E-OBS performance are summarized with respect to the circulation types, and spatial distribution of the rainfall and air temperature, calculated with these two sources, is compared, using the common E-OBS spatial resolution.
3.1 WRF and E-OBS Comparison with Meteorological Measurements
For the rainfall, the E-OBS is in closer agreement with the measurements in terms of all three statistics considered. The WRF model significantly overestimates the observed rainfall, while the E-OBS gridded values are slightly underestimated. There is also a clear seasonal pattern in the WRF model performance for rainfall. The largest differences between the WRF modelled and the observed daily precipitation are for summer months, when the convective rainfall dominates the total precipitation. For this season, local, intensive rainfall episodes may contribute to the majority of the monthly precipitation sum, and these events are likely to be missed or shifted both in time and space by WRF because of its local character.
For the rainfall, grid-to-grid correlations are smaller, and are close to 0.5, both for January and July. The WRF maps also show higher rainfall values, compared to E-OBS, with mean differences of 14 and 41 mm for January and July, respectively. Both WRF and E-OBS show the highest rainfall for the mountainous areas in the south. However, the second area of increased rainfall, located in northern Poland, is shifted westward in the E-OBS maps, when compared with WRF. This is both for January and July.
3.2 Circulation Types Analysis
Circulation types and groups of circulation types determined using ACCT for the 1981–2010 period
Group of circulation types
Circulation types belong to the group
Lower anticyclonic, upper anticyclonic dry
SWAAD, NWAAD, NEAAD, SEAAD
Lower anticyclonic, upper cyclonic dry
SWACD, NWACD, NEACD, SEACD
Lower cyclonic dry
SWCCD, NWCCD, NECCD, SECCD, SWCAD, NWCAD, NECAD, SECAD
Lower anticyclonic, upper anticyclonic wet
SWAAW, NWAAW, NEAAW, SEAAW
Western, lower anticyclonic, upper cyclonic wet
Eastern, lower anticyclonic, upper cyclonic wet
Western, lower cyclonic wet
SWCCW, NWCCW, SWCAW, NWCAW
Eastern, lower cyclonic wet
NECCW, SECCW, NECAW, SECAW
Unidentified direction of advection/without advection
XXAAD, XXAAW, XXACD, XXACW, XXCAD, XXCAW, XXCCD, XXCCW
The WRF model overestimates the measured rainfall for all circulation types. This is especially the case for eastern or unidentified direction of advection (e.g. ECdW, EACW and XX; Fig. 11). Especially for group XX, ME shows almost two times higher rainfall in the period 1981–2010 than the measured value. On the contrary, the E-OBS database shows significant underestimation for the XX group. The MAE and IOA also show worse performance of the WRF model for rainfall, compared to the E-OBS database, and MAE is especially large if the eastern wet types are considered (e.g. ECdW). For dry circulation types, MAE for the WRF model is below 1.5 mm/day, and for the majority of circulation types, the E-OBS MAE does not exceed 1.0 mm/day, except for the wet types of eastern advection. There were similar findings for the IOA statistics, which was higher for all the circulation types for E-OBS, compared to WRF. Both E-OBS and WRF show the lowest IOA for XX.
For precipitation, the differences between groups of circulation types are the highest for summer and winter (differences exceed 2 mm/day for the median value). The dispersion of precipitation values, according to the interquartile range, is the highest for the wet-type group, especially for AAW, WACW and WCdW. Days with advection from the east (ECdW, EACW) have a large interquartile range, except for the winter months. The outliers here reach 100 mm/day. There is a strong seasonal variability in the interquartile range for all groups of atmospheric circulation types. The values vary from 1.4 to 3.6 mm/day for winter months, to 3.2–7.2 mm/day for autumn. In summer, the interquartile range for most of the circulation-type groups is in the range from 5.2 to 7.0 mm/day and reaches the maximum (14 mm/day) for group ECdW, which contains the types with unstable convective air masses.
The box plots presented in Figs. 12 and 13 also show the differences in the WRF and E-OBS data and the observations. This is especially true for the outliers, which were covered in the general model performance statistics presented above. For the air temperature and the winter months, there is a close agreement between the E-OBS and the measurements for the group types with high frequency (AAD, AAW, ACD, CdD) in terms of quartiles and the high outliers (Fig. 12). For groups ACD and CdC, the WRF model also shows a very good agreement with the measurements, but for the lower and upper anticyclonic group types (AAD, AAW) and most of the less frequent groups of circulation types, the median temperature together with the first and third quartile is lower, compared with the measurements. The WRF model, in general, better reproduces the lower outliers when compared with E-OBS, and for the majority of the groups in winter, the lower outliers are underestimated by WRF and overestimated by E-OBS. For the less frequent type of group, excluding XX, the WRF model shows closer agreement with the measured air temperatures than the E-OBS..
For spring and summer, both E-OBS and the WRF models reproduce the air temperatures well, especially for the wet type (Fig. 12). For summer, the WRF model overestimates the median value for the majority of the circulation types. The differences between the temperature quartiles from WRF and observed datasets are small and usually do not exceed 1 °C. For groups AAW and WCdW, the WRF model shows higher maximum values of air temperatures, compared with the measurements, for both spring and summer. In summer, WRF shows closer agreement with the measurements for the low outliers, but, similar to E-OBS, the model overestimates air temperature. In spring, the WRF model reproduces well the higher air temperatures.
For autumn, the WRF model reproduces well the median value both for frequent and rare circulation types. The high values are usually overestimated, especially for the most common circulation types of AAD, AAW and ACD, for which the E-OBS shows a closer agreement with the measurements than WRF. However, for the less frequent groups, like WCdW or EACW, the upper outliers are better represented by the WRF model.
For rainfall, the box plots show that for the WRF model there is a significantly higher number of days with rainfall, compared to the measurements (Fig. 13). On the contrary, the E-OBS data give a smaller number of days with rainfall compared to the measurements. Regardless of the season, the median values of precipitation for WRF data are in a better agreement with the measurements than E-OBS for the majority of the circulation types, especially in groups AAW, WCdW and ACD. For summer, the WRF model gives higher daily rainfall for all the circulation types. The same is observed for winter and spring in the lower cyclonic types of the group. For autumn, the WRF median is higher, compared to the measurements only for AAW and ECdW.
For the majority of the circulation types and regardless of the season, the first quartile for the WRF model is about 0.1 mm higher, compared with the observations, while this difference for E-OBS exceeds 1.0 mm, especially for the spring and summer months (Fig. 13). For higher daily rainfall values, represented by the third quartile, the E-OBS is in better agreement with the measurements for all circulation types, except for the autumn and winter months. For all the circulation types, the third quartiles of precipitation in the WRF model are 0.5–2.0 mm higher than in the observed datasets in spring and summer. In the winter and autumn months, they are about 1.0 mm lower, but only for lower anticyclonic groups of circulation types, with any differences in other groups. The WRF model also shows higher than measured highest daily rainfall values for all the circulation types, but these extreme values are in closer agreement with the observation than for E-OBS.
4 Summary and Conclusions
In our study, we have presented the results of high-resolution dynamical downscaling of daily rainfall and air temperature with the regional climate model WRF. The results were evaluated by comparison with measurements and gridded data. Additionally, we presented the method for automatic classification of atmospheric circulation types, which utilize the high-resolution WRF model output. The method for the automatic classification of the circulation types (ACCT) for Poland, based on the WRF data (wind direction, cyclonality and humidity of air masses), was applied for the entire period of 1981–2010. The WRF model performance was evaluated for daily precipitation and air temperature, individually for each month and also with respect to the group of circulation types. The analysis of the regional dynamical downscaling with the WRF model was complemented by comparison with spatial data obtained with the geostatistical method (E-OBS).
A tool for automatic derivation of circulation schemes was developed and used with high-resolution gridded meteorological data. The tool is flexible in terms of spatial domain resolution, location and meteorological input. The advantage of ACCT classification is that it provides the opportunity of type grouping depending on the research aim, while in long time series, a large number of circulation types permit detailed case studies. The classification scheme can also be extended by incorporating other classification criteria.
The variability of the air temperature and precipitation between particular types and groups of circulation types confirmed the usefulness of the classification methodology. The worst results were connected with the anticyclonic group type and with the types with unstable convective air masses. The authors are aware that worse results, in terms of larger variability of meteorological parameters, could be caused by the difficulty in choosing the mode value of classification types for the large area of Poland, with its considerable spatial variability. The classification scheme will therefore be modified to allow for spatial variability of circulation type for a given day within the area that is analysed.
In general, the WRF model shows a good agreement with the observed daily air temperature, especially for its lowest values. The higher air temperatures are, except for the winter months, overestimated. The error statistics of ME, MAE and IOA for WRF also show a better model–measurements agreement compared to E-OBS. The E-OBS overestimates the lower air temperatures in most seasons, which might be linked with the coarser spatial resolution compared to WRF. The spatial patterns of the air temperature and rainfall are similar for both WRF and E-OBS, when the long-term mean values are compared. The WRF model shows a larger spatial variability because of the higher spatial resolution.
The close WRF–measurements agreement, quantified by ME, MAE and IOA for the air temperatures, is independent of group of circulation types. However, there is a seasonal variability in temperature agreement in particular circulation types considered. In contrast to small overestimation for all circulation types for spring and autumn, the winter months are underestimated, especially for frequent lower and upper anticyclonic groups of types (AAW, AAD). For all the seasons, except the summer, the WRF model shows lower, compared with the measurements, minima of the air temperature. In winter, the WRF model better reproduces the air temperatures for the coldest groups of circulation types (ACD, CdD). For warmer groups of circulation types in winter and autumn, E-OBS is in better agreement with the measurements. In spring and summer months, the mean air temperatures are in closer agreement with the measurements for the WRF model.
The error statistics for the rainfall shows a worse performance of the WRF model, compared with E-OBS. This is especially the case for the summer months. The WRF model overestimates the measured rainfall, especially the higher daily values. The WRF model also gives more days with rainfall, compared to the measurements. However, the E-OBS underestimates the precipitation values mainly in the summer and autumn months.
The differences between WRF and E-OBS for precipitation are similar in all groups of circulation types. For the spring and summer months, the WRF model overestimates the daily precipitation sums. This overestimation is especially large for the XX and eastern groups of circulation types. The maximum sums of precipitation for WRF are higher than the observed values, but are in better agreement with the measurements when compared to E-OBS. In winter and autumn, the modelled precipitation sums are close to the measurements (e.g. in WCdW) or underestimated for the lower anticyclonic-type groups.
The spatial distribution of the differences between the WRF and E-OBS data changes significantly according to the atmospheric circulation type. This is of significant practical importance, as the large-scale atmospheric circulation pattern determines the uncertainty related to the meteorological information provided by the WRF model.
In this work we have shown that the WRF model performance depends strongly on the type of atmospheric circulation. This is especially the case for rainfall. This suggests that the model evaluation should also consider some indices related with circulation types, as presented in this study. Also, it means that the circulation type can be used to assess the uncertainty related with the numerical weather forecasting. High-resolution WRF model data can be used to determine the circulation types using the ACCT, with respect to, e.g. humidity of air mass.
The overall poor WRF model performance for rainfall shows the need for improvement. The uncertainty in the WRF model prediction for rainfall is high and changes both seasonally and with circulation type. The model performance could be improved, e.g. by data assimilation (e.g. GNSS data, as suggested by Schwitalla et al. 2011).
This work was supported by the National Science Centre (NCN), Poland (Grants Nos. NN404 014 740 and UMO-2011/03/B/ST10/06226). Calculations have been carried out in the Wroclaw Centre for Networking and Supercomputing (http://www.wcss.wroc.pl), Grant No. 170. Meteorological measurements for this work were provided by the Institute of Meteorology and Water Management, National Research Institute.
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