Datasets

The modelling framework outlined in Chapter 3 was used to assess a range of P datasets, including ground-based gauges, climate reanalysis and remote sensing products. Each has its own set of spatiotemporal resolution and extent characteristics, ranging from 1 to 80 km cell sizes and hourly to daily time steps. The availability of each source is also highly variable, with

reanalysis data having continental to global reach in an uninterrupted series and hourly gauge data being limited to point locations with relatively short and sometimes discontinuous records. For all rainfall product test simulations, the Digital Elevation Model (DEM) was taken directly from the 90m HydroSHEDS product. Further details can be found on HydroSHEDS in Section 4.1.2. Simulation results were compared with both flow gauge data and satellite observations for the period 12 November - 12 December 2015 which encompasses Storm Desmond (3-8 December 2018) across Northern England. All precipitation data has been downloaded directly from the internet and is freely available for use in research. Some intermediary steps were required to translate the original formats into postgres tables which are explained in Section 3.1.1. The datasets themselves, summarised in Table 12 will now be discussed in more detail.

Table 12 - Precipitation data time intervals and spatial resolutions

DATASET TIME INTERVAL RESOLUTION

MIDAS 1 hour 1 km

ERA Interim 3 hour ~80km

ERA 5 1 hour ~30km

EOBS 24 hour ~30km

MSWEP 3 hour ~30km

5.1.1.1 MIDAS

The MIDAS dataset, outlined in Section 4.1.1, was used as a benchmark local precipitation dataset. In a best case scenario, local, high quality rainfall data would always be used. However, long enough usable records at a suitable density are not always available, particularly in developing areas. Therefore lower resolution datasets, as outlined in the following sections, often provide the only options.

5.1.1.2 ECMWF Re-analysis

The European Centre for Medium-Range Weather Forecasts (ECMWF), based in Reading, is the largest provider of re-analysis products in Europe. Each describes a unique time period

with varying spatial and temporal resolutions using a collection of climate observations. Data from satellites, ground-based instruments and observers is used to drive computational models of the atmosphere and ocean and generate estimates for a range of variables at every specified grid cell and time step. ERA40 (Uppala et al., 2005), the initial attempt to produce a coherent re-analysis product, was the largest experiment of its kind and was highly influential at the time, however has since been superseded by the vastly improved ERA-Interim (Dee et al., 2011). The most recent release from ECMWF was ERA5 in July 2017, which is still under continued development. Compared with ERA Interim, ERA5 supports an increased spatial and temporal resolution and eventually will replace the earlier dataset. Both provide estimates of a large number of climatic variables up until the present day. The major differences are shown in Table 13. From both datasets, the variable used was total precipitation.

5.1.1.3 E-OBS Station Interpolation

E-OBS is a 0.25 degree resolution gridded daily dataset providing temperature, pressure and precipitation information across Europe (Haylock et al., 2008). The spatial grid was designed to match the monthly Climate Reanalysis Unit (CRU) dataset. Monthly totals are interpolated first, then indicator and universal kriging is used to interpolate the daily values before monthly and daily estimates are combined. The number of gauges included in interpolation depends on the time step and can range between 500 and 2000.

To assess how well extremes are captured in the final product, a cross-validation dataset was created by selectively removing each station, using its neighbours to interpolate the missing records and comparing the interpolated series to the observed data. The median reduction factor for precipitation using the cross-validation data was found to be 0.66, demonstrating the large impact of interpolation on extremes. Hofstra et al (2009) carried out further testing of E-OBS using local datasets with denser gauge networks and highlighted the need for

understanding of the limitations of accuracy in the interpolated product. In the UK, E-OBS was compared to a 5 km resolution daily dataset provided by the Met Office, interpolated using 4400 stations. The RMSE of E-OBS when compared to the Met Office data was 2.17 mm daily, which is more consequential for water supply as a resource than flood volumes.

Table 13 - Differences between ERA-Interim and ERA5 (ECMWF, 2019)

ERA-INTERIM ERA5

Temporal

Resolution 3-hourly 1-hourly

Spatial

resolution 79 km 31 km

Period covered 1979 - present 1950 - present

Input

observations As in ERA-40 and from Global Telecommunication System In addition, various newly reprocessed datasets and recent instruments that could not be ingested in ERA-Interim

Model input As in operations (inconsistent SST) Appropriate for climate (e.g. CMIP5

greenhouse gases, volcanic eruptions, SST and sea-ice cover)

Production

Period August 2006 – end 2018 Jan 2016 – end 2017, then continued in near real-time

Assimilation

system IFS Cycle 31r2 4D-Var IFS Cycle 41r2 4D-Var

Satellite data RTTOV-7, clear-sky, 1D-VAR rainy

radiances RTTOV-11, all-sky for various components

Variational bias

scheme Satellite radiances Also ozone, aircraft and surface pressure data

5.1.1.4 MSWEP Ensemble

The Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset from Princeton University offers a weighted-ensemble of re-analysis products, gauge interpolations and satellite observations at 0.1 degrees (Beck, Van Dijk, et al., 2017). The initial version provided 3 hourly rainfall on a 0.25 degree grid before being upgraded to the higher resolution second and current version (Beck, 2017). MSWEP has undergone quality control to ensure erroneous gauges are fully or partially excluded. This process included checking the cumulative distribution function, daily totals, and correlation with gridded datasets and number of unique values, among other steps. The satellite and reanalysis data were tested against ground-based

gauges using 3-day averages. Bias correction was then carried out using the number of wet days in the gauges and reanalysis or satellite datasets.

When tested using hydrological modelling (HBV) against a range of other similar precipitation products, including ERA Interim, MSWEP performed the best on NSE, R2_{, mean absolute error} (MAE) and bias among other estimates. In terms of 99.9th _{percentile error, which is the most} relevant for flooding applications, ERA-Interim outperformed version 1 of MSWEP with 27.82 to 29.30 mm/d, until the latest version was released with only 14.90 mm/d.MSWEP is freely available online at gloh2o.org.