Storm Desmond

Storm Desmond caused severe flooding, travel disruption and a power outage across northern England, parts of Scotland and Ireland on 5 December 2015. Cumbria in northwestern part of England is one of the worst affected regions with more than 200 mm of rain in 24 hours recorded in that area. Storm Desmond broke the United Kingdom's 24-hour rainfall record, with 341.4 mm of rain falling in Honister Pass, Cumbria. On Saturday, 5 December, UK Met office issued a red warning of heavy rain for Cumbria. The cyclone also led to flooding in southern Norway.

Orographical enhancement of the precipitation played the major role in the event and the operational model of ECMWF picked well the highest rainfall amounts over the orographical barriers. However, the forecast underestimated the peak values of about 100 mm in 24 hours in Cumbria and overestimated the precipitation amount in lee of the hills (Figure 2).

Model experiments

Several experiments have been conducted with OpenIFS for both cases with the aim to test the effect of starting date and forecast length, initial condition as well as spatial resolution to the forecast quality. Details of the experiments are summarized in Table 1.

!! The input data and the namelists needed to run the experiments can be downloaded from the ECMWF FTP server: download.ecmwf.int/test-data. !!

The files are packed in .tar.gz files and structured into directories named after the case studies (i.e., Xaver_201312, Desmond_201512) and subdirectories indicating the main experiment characteristics (e.g., T255L91_ERA5). Each archive file contains the data for a given starting date (e.g., gs0c_2015120300.tar.gz). Typical content of a .tar.gz file is as follows:

The namelist file highlighted in green in the box above controls the necessary settings (e.g., time step, experiment ID) as well as the post-processing. The most important namelist elements are listed below with their explanation:

In the evaluation, we would like to investigate the following variables:

• 2-meter temperature: its GRIB code number is 167;
• precipitation: it is composed from large-scale and convective precipitation with code numbers 142 and 143, respectively;
• mean sea level pressure: its code number is 151;
• 10-meter wind gust: its code number is 49;
• temperature at 850 hPa level: its code number is 130;
• relative humidity at 700 hPa level: its code number is 157;
• geopotential at 500 hPa level: its code number is 129;

• u and v wind components at 250 and 100 hPa with code numbers 131 and 132, respectively.

The listed variables have to be included in the namelist among the post-processing variables (see the code numbers and levels highlighted with bold characters in the box above). More information about the namelist settings and GRIB field codes can be found in the OpenIFS how-to articles: How to control OpenIFS output.

!! To run the model, the paths of the input data and the namelist have to be set in the job. ..... Acceptance testing OpenIFS after installation.

Preparation of data for visualization

After the job run is completed, the outputs are split into 2 files (located in the working directory): ICMGG* files are for the gridpoint fields and ICMSH* files are for the spectral ones. The two files should include all the necessary variables set in the namelist. However, before visualisation of the results, some steps are still needed.

Post-processing of model outputs

The Metview macros prepared for visualisation of experiment results desire the meteorological variables in GRIB files separated by variables and days with the following file names:

• t2_${day}.grib for 2-meter temperature,
• p_${day}.grib for precipitation,
• mslp_${day}.grib for mean sea level pressure,
• gust_${day}.grib for 10-meter wind gust,
• t850_${day}.grib for temperature at 850 hPa,
• q700_${day}.grib for relative humidity at 700 hPa,
• z500_${day}.grib for geopotential at 500 hPa,
• u250_${day}.grib for horizontal wind components at 250 hPa,
• u100_${day}.grib for horizontal wind components at 100 hPa,

where day is the given date in format of yyyymmdd (e.g., 20151203). All the files should contain data for 8 timesteps per day (i.e., in every 3 hours).

The 2-meter temperature, the precipitation, the mean sea level pressure and the wind gust are expected in gridpoint representation. So they will be prepared from the raw ICMGG* outputs with the next operations:

where expID is the 4-digit experiment ID and step is the post-processing step (every 3 hours). For precipitation, both the convective and large-scale precipitation components have to be in the same file:

The pressure level data are required in spectral representation. So they will be prepared from the raw ICMSH* outputs with the next operations:

For wind, both the u and v components have to be in the same file:

The size of the resulted files varies by the spatial resolution and the representation of the data. For instance, the file size at T255L91 resolution is 10 MB and 8 MB per variables for gridpoint and spectral fields, respectively, whereas these values increases to 35 MB and 26 MB at T639L137, to 233 MB and 179 MB at T1279L137.

Preparation of reference data

As reference data, we will apply the ECMWF re-analyses. Both ERA-Interim and ERA5 datasets are available for the users and can be downloaded from the ECMWF MARS (Meteorological Archival and Retrieval System) system. (Please note that the figures above are based on station observations which are however not publicly accessible.) Re-analyses are created by optimal combination of available observational information and short-range numerical weather predictions using data assimilation techniques. They provide the most comprehensive description of the past and current states of the 3-dimensional atmosphere or the Earth system.

ERA-Interim dataset (Berrisford et al., 2011) was prepared on 79 km horizontal resolution with 60 vertical levels starting from 1979. Analysis fields were constructed in every 6 hours using variety of observations (conventional measurements, remote sensing data, air craft measurements etc.), the 4D-Var data assimilation technique and the IFS model version which was operational in 2009 (cycle 31r2). The forecasts initialized from the analysis produced 3 hourly outputs up to 24 hours. 

ERA5 (Hersbach and Dee, 2016) is being constructed on higher, 32 km horizontal resolution with 137 vertical levels from 1950. Analysis fields are being prepared hourly with inclusion of newly reprocessed observational data, using the 4D-Var data assimilation technique and the IFS cycle 42r1 model version. ERA5 forecasts initialized from the hourly analyses produce hourly outputs up to 18 hours and give an estimation of forecast uncertainty. There is an important difference between ERA-Interim and ERA5 in handling of the accumulated parameters: in ERA5 the accumulation is calculated from the previous post-processing step (i.e., along one hour), while in ERA-Interim it is from the beginning of the forecast – this feature will have relevance in evaluation of the precipitation amount and wind gust. (More information about the characteristics of ERA-Interim and ERA5 can be found in the Copernicus Knowledge Base: What are the changes from ERA-Interim to ERA5?)

The Metview macros prepared for visualisation of re-analysis data desire the meteorological variables in GRIB files separated by variables for a time period with the following names:

• ${dataset}_t2_${period}.grib for 2-meter temperature,
• ${dataset}_p_${period}.grib for total precipitation,
• ${dataset}_mslp_${period}.grib for mean sea level pressure,
• ${dataset}_gust_${period}.grib for 10-meter wind gust,
• ${dataset}_t850_${period}.grib for temperature at 850 hPa,
• ${dataset}_q700_${period}.grib for relative humidity at 700 hPa,
• ${dataset}_z500_${period}.grib for geopotential at 500 hPa,
• ${dataset}_u250_${period}.grib for horizontal wind components at 250 hPa,
• ${dataset}_u100_${period}.grib for horizontal wind components at 100 hPa,

where dataset is a 2-digit identifier of the re-analysis data, ei for ERA-Interim and eafor ERA5; period is the investigated time period in format of yyyymmdd-yyyymmdd (e.g., 20151201-20151206 for Desmond).

The 2-meter temperature, the precipitation, the mean sea level pressure and the wind gust are expected in gridpoint representation, while the pressure level data are required in spectral representation. Total precipitation and wind gust as accumulated parameters derive from forecasts, all the other variables are real analyses. Consequently, the daily quantities for precipitation and wind gust are composed of 8 and 24 timesteps from ERA-Interim and ERA5, respectively, the other variables have 4 and 8 timesteps (recall that output frequency of the forecast experiment is 3 hours). Besides the two (large-scale and convective) precipitation components, total precipitation is also available for direct retrieve both in ERA-Interim and ERA5, with GRIB code 228.

To download the necessary data from MARS, the following steps have to be accomplished:

1. To have access to the ECMWF public datasets, an account is needed to the ECMWF web site: https://apps.ecmwf.int/registration/.
2. To retrieve data from MARS, an ECMWF key is needed to be downloaded and installed. This page shows that step by step: Access ECMWF Public Datasets.
3. Having the key, the scr_download_re-analysis shell script has to be run. It is available in the OpenIFS repository (for cycle 40r1) and using getmars for retrieve.

The re-analysis source (i.e., ERA-Interim or ERA5), the surface and pressure level variables to be retrieved, the period of the data and the output directory have to/can be specified for the scr_download_re-analysis script. Calling it with -h option, it gives a detailed help with some examples at the end (but calling it without any option, it also gives a short instruction to its configuration):

% ./scr_download_re-analysis -h
 ----------------------------------------------------------------------
        This script downloads surface and pressure level ERA-Interim or ERA5 re-analysis data for a given time range from MARS.
        Usage:
        -c[class],-s[surface_variables],-p[plevel_variables],-f[firstdate],-l[lastdate],-o[output_directory]
        -h/-help

        Examples:
       ./scr_download_re-analysis -cei -s"t2 p mslp gust" -p"t850 q700 z500 u250 u100" -f20151203 -l20151205 -o"../reference"
       ./scr_download_re-analysis -cea -sall -p"t850 q700 z500 u250 u100" -f20151203 -l20151205 -o"/home/rd/digs/metview/paper_OIFS/input/reference"
       ./scr_download_re-analysis -ce5 -s" " -p"t850" -f20151203 -o"/home/rd/digs/metview/paper_OIFS/input/reference"
 ----------------------------------------------------------------------

The retrieved ERA-Interim fields occupy approximately 54 MB, while ERA5 fields take 700 MB for the case Desmond (i.e., for 1–6 December 2015).

Visualization with Metview

The visualization package contains the following folders and file:

Using the Metview macros

The visualisation can be done by the Metview macros (*.mv files) in the ways: (1) interactively using a dialogue box or (2) in batch mode.

(1) Interactive dialogue box

With right click on the macro and then selecting the Execute option from the menu, the settings can be seen in a dialogue box:

(2) Batch mode

In batch mode the macro can be executed following the next syntax:

where macro is the macro to be run (plot_raw_IC.mv, plot_forecastrun.mv or plot_ERAI_ERA5.mv); option1, option2 etc. are the settings listed above. A detailed help together with some useful examples is provided with simple execution of the macro:

The shell script scr_run_macros executes the macros from the UNIX/Linux shell and it can be tailored for the own needs.

External functions and macros

The prepared Metview macros use some external functions, macros and colour definitions which are placed in the definitions directory.

To reach these functions and colour definitions, the path of the definitions directory has to be added to the METVIEW_MACRO_PATH (e.g., in .bashrc).

Input data

The input data are requested with the following content, format and name convention:

• Macro plot_raw_IC.mv expects the raw ICM* files as input: ICMCL${expID}INIT, ICMGG${expID}INIT, ICMGG${expID}INIUA, ICMSH${expID}INIT, where expID is the 4-digit experiment ID.
• Macros plot_forecastrun.mv and plot_ERAI_ERA5.mv expect grib files as input with the following file names: ${variable}_${date}.grib, where variable can be t2, mslp, p, gust, t850, q700, z500, u250, u100; date is day in format yyyymmdd.

Output figures

All the macros produce figures in single-page .ps files with the following file names:

• plot_raw_IC.mv: ${variable}_${level}_ERAI-ERA5_${date}+${timestep}.ps,
  where variable can be stl2, lnsp, z, t, cc, u, q; level can be 0 (in case of surface variables) or from 1 to 91; date is day in format yyyymmdd; timestep is forecast lead time in hours, e.g., 0, 3, 6 etc.
• plot_forecastrun.mv: ${variable}_${level}_${expID}_${date}+${timestep}.ps,
  where variable can be t2, mslp, p, gust, t, q, z, u; level  can be 0 (in case of surface variables) or 850, 700, 500, 250, 100; expID is the 4-digit experiment ID; date is day in format yyyymmdd; timestep is forecast lead time in hours, e.g., 0, 3, 6 etc.
• plot_ERAI_ERA5.mv: ${variable}_${level}_${reference}_${date}.ps,
  where variable can be t2, mslp, p, gust, t, q, z, u; level can be 0 (in case of surface variables) or 850, 700, 500, 250, 100; reference can be ERAI or ERA5; date is day in format yyyymmdd.

Running the Metview macros results in a large number of figures. To have an overview on them, a catalogue can be prepared which contains all relevant plots for the selected variable and a given investigation aspect in a concise format (see Figure 4 for illustration). To quickly generate this kind of album, the Macro functionality of the Microsoft Office programme can be used.

References

Berrisford, P., Dee, D.P., Poli, P., Brugge, R., Fielding, K., Fuentes, M., Kållberg, P.W., Kobayashi, S., Uppala, S., Simmons, A., 2011: The ERA-Interim archive Version 2.0. ECMWF ERA Report Series, 27 p. [PDF]

Hersbach, H., Dee, D.P., 2016: ERA5 reanalysis is in production. ECMWF Newsletter 147, p. 7.

Hewson, T., Magnusson, L., Breivik, O., Prates, F., Tsonevsky, I., de Vries, H.J.W., 2014: Windstorms in northwest Europe in late 2013. ECMWF Newsletter 139, 22–28. [PDF]