Introduction
The European Centre for Medium-range Weather Forecasts (ECMWF) produces a range of forecasts, among them a 10-day
deterministic high-resolution forecast (HRES) and a lower resolution 15-day
51-member ensemble prediction system (ENS) that is extended to 46 days twice
weekly (Mondays and Thursdays at 00:00 UTC; ). In this paper
we refer to the extended ENS as ENS-ER. On longer time ranges ECMWF issues a
seasonal ensemble forecast system (SYS4), operational since November 2011.
SYS4 issues a 7-month prediction (extended to 13 months four times a year)
once every month . The ENS-ER forecast system benefits
from frequent updates of the model physics and data assimilation system
. ECMWF releases official model updates on average
2–3 times a year which typically include improved schemes for physical processes,
better use of observations and their assimilation, and sometimes an increase in
model resolution. The seasonal forecast has a lower resolution, is an older
model version than ENS-ER and is also updated much less frequently. This
implies that the skill of the seasonal forecasting system is lower relative
to ENS-ER in the overlapping first 6 weeks.
Applications that use numerical weather predictions as forcing, such as the
operational European Flood Awareness System (EFAS;
) are often designed for a
specific purpose. EFAS has, since the start, focused on early warning of floods
in the medium-range forecast horizon, typically up to 15 days. Recently, a
seasonal hydrological outlook forced by SYS4 was implemented operationally
with a lead time of 7 months .
This extension to the monthly and seasonal timescales is potentially very useful
in order to (i) produce products which extend the previous forecast horizon,
(ii) benefit from hindcasts for pre- and post-processing to produce output of
higher quality (e.g., model-based return periods) and (iii) design completely
new early-warning frameworks complementing the existing ones. The extended
lead time provided by running EFAS forced by weather prediction across
different timescales could potentially provide added benefit in terms of
very early planning, for example for agriculture, energy
and transport sectors , as well as water resources
management . Such a forecast system would be a first step to
close the identified gap between hydrological forecasts on the medium (up to
15 days) and seasonal range . These extended-range systems
may not be able to capture extremes of short-lived events like floods but
they are able to detect anomalous conditions on longer lead times, such as
low flows and droughts .
The concept of seamless forecasts was first introduced by .
formally expanded the idea showing how short-lived
phenomena under certain conditions may persist and increase predictability at
longer timescales. Since then the concept of a unified or seamless framework
for weather and climate prediction has been vastly debated
. However, as noticed by in his
seminal paper: while “the atmosphere knows no barriers in time-scales”,
model implementation is often segmented for practical reasons. Still, major efforts
have been made to create unified systems. Indeed, the ENS-ER was the first
attempt to create a seamless extension of the ECMWF medium-range forecast to
the monthly timescales . Similarly, the UK Met Office has, in the
past 25 years, worked to create a unified model that could work
across all timescales . Also the climate community has moved
in the same direction. For example, the EC-Earth project shows that a bridge
can be made between weather, seasonal forecasting and beyond
.
The latter projects went all the way to create new systems starting from
existing components and were therefore costly and time demanding. In
contrast, a practical and simpler approach could be taken. The seamless idea
could be translated into a concatenation of “the best” forecast at each
lead time. The clear advantage of this off-the-shelf seamless prediction
conversion is that it uses products that are already available and
operational, thereby avoiding the complications of new developments, while at
the same time generating forecast products that meet the demands of different types of users
. However, there is an underlying complexity in this
simplification; the difference in design between the various forecasting
systems makes the concatenation not entirely straight-forward. The
forecasting systems are related since they are from different generations of
the same model development; however, they have non-matching temporal and
spatial resolutions, different hindcast span and different ensemble sizes.
One important consequence of this is that the more frequent updates to the
extended range compared to the seasonal forecasting system at ECMWF causes
the model errors from the two systems to diverge over time, and only closing
this gap when the seasonal system is updated to a newer model version
. Then model outputs either need to be bias-corrected to
be a useful forcing to drive sectoral models such as EFAS or final
products should be provided in terms of anomalies calculated against the
model climate, taking into consideration the bias of the seamless forecast
system. In both cases the seamless system needs to account for the use of the
hindcast dataset and the application of some bias correction algorithm. In
return, the advantage is in the gain in skill and the extension of the
lead time.
In this work the benefit of a seamless hydro-meteorological system was tested
for a span of time ranges from 1 week to 6 months for stream flow forecasts
over the European domain using the EFAS system. The aim was to test whether
integrating medium-range forecasts with seasonal prediction contributes to
enhance hydrological predictability on the seasonal timescale. Specifically, the
questions addressed were the following. What is the gain, in terms of hydrological
forecasting, when using a more recent model version in the first 46 days
provided by the use of the ENS-ER? What is the skill gain provided by having
more frequent forecast updates?
Methods
Hydrological model system
Technical details of the forecast and the hindcast used in this paper.
System
Time res.
Spatial res.
Horizon
Ensemble size
Issue frequency
Hindcast set
Hindcast ensemble size
ENS-ER
3 h/6 h
18/36 km*
46 days
51
Twice weekly
20 years
11 members
SYS4
6 h
80 km
7/13 months
51
Monthly
30 years
15/51 members
SEAM
6 h
5 km
6 months
51
Twice weekly
20 years
11 members
* The resolution changes to 36 km at day 16 of the forecast.
The hydrometeorological system used in this study was the European Flood
Awareness System (EFAS; ).
EFAS is an operational early-warning system covering most of the European
domain and has been run operationally since October 2012 as part of the
COPERNICUS Emergency Management Service (CEMS). The hydrological component of
EFAS is the distributed rainfall-runoff model LISFLOOD
. LISFLOOD calculates the main
hydrological processes on sub-daily and daily timescales that generate
runoff for each grid cell. In the operational setup, EFAS covers most of
Europe on a 5 km × 5 km equal-area grid. The runoff is transformed through a
routing scheme to estimate the river discharge at each grid cell along the
river network. The routing scheme also takes into account water retention in
lakes and reservoirs. This study will concentrate on the forecast of river
discharge at the outlets of the sub-basins of the river network that were
used for calibration of the current EFAS system
. The total number of outlets used was 679, and
they represent river basins of all sizes and characteristics across the EFAS
domain.
In its operational implementation the latest calibration (referred to as
“tuning” in the numerical weather prediction, NWP, nomenclature) of LISFLOOD used an observational dataset of
meteorological forcing data (precipitation and temperature) and observed
discharge covering the model domain over the period 1990–2013
. The meteorological dataset comprises more
than 5000 synoptic stations that have been interpolated to a 5 km × 5 km Lambert
azimuthal equal-area projection . The high-resolution
gridded observations of precipitation and temperature were used for the
calibration of LISFLOOD. The observational meteorological dataset was also
used to generate a reference modeled climatology of discharge (hereafter
called water balance, WB) which is used as (i) initial conditions for the
operational forecast and hindcasts and (ii) a reference model run to assess the
performance of the forecasts. Using the WB run as proxy observation
simplifies the interpretation of the skill scores as it avoids the
complication of having to assess the bias against observed discharge. The
purpose of this paper is rather to assess the skill of the two forecasts used
for forcings rather than the total skill of the forecasting system.
Schematic overview of the operational ECMWF ensemble forecast for the extended range
and its associated hindcast. The hindcasts consists of a reduced ensemble forecast (11 members)
with the same starting date of year as the current forecast, but run for the previous 20 years.
Seamless integration of meteorological forcing data
Every Monday and Thursday, ECMWF issues an extended-range ensemble forecast
(ENS-ER) by continuing the integration time beyond day 15 up to day 46, with
a lower-resolution model (Fig. , Table ). Each ENS-ER integration comes with an 11-member
hindcast set produced for the same dates as the forecast date over the
previous 20 years. This hindcast set provides identical integrations as the
current operational forecast with the difference that ERA-Interim reanalysis
(ERAI; ) and ERAI land reanalysis are
used to provide the initial conditions, whereas the operational ensemble
forecast uses the operational analysis. The hindcast data together with
observations can be used in many applications, for example to calibrate the
forecast in an operational setting .
Schematic overview of the seasonal, extended-range forecast and merged systems.
The extended forecast is issued every Monday and Thursday and extends up until 46 days, the
seasonal forecast is issued on the first of each month and extends up until 7 months (13 months
in February, May, August and November). The merged forecast concatenates the latest extended
forecast with the latest seasonal forecast.
Continuous ranked probability skill score (CRPSS) for (a) merged forecast against seasonal
forecast for all start dates evaluated over the 679 basin outlet points; (b) as in (a) but only for
the first merged forecast of each month; (c) merged forecast against climatology for all lead times
in blue and (d) as in (c) but for the first merged forecast in the month. The shaded blue area denotes
the 10–90 percentile of the CRPSS and the blue line the median. The black solid (dotted) lines in
(c) and (d) denote the mean and 10–90th percentiles of the CRPSS of the seasonal against the
climatological forecast.
The operational seasonal forecast (SYS4) issues a new forecast at the
beginning of each month with a lead time up to 7 months, four times a year
extended to 13 months (Fig. ). SYS4 has a hindcast
consisting of 30 years starting at each month and consisting of 15 members.
The new seamless forecasting system (hereafter called SEAM) was created by
concatenating each ENS-ER ensemble member with a randomly selected SYS4
ensemble member at day 46, which is the last day of the ENS-ER (Fig. ).
SEAM benefits from the frequent updates of the ENS-ER and
has the 7-month horizon of the seasonal system.
The number of weeks (days) before the CRPSS goes below zero using only the first forecast
of the month for (a) SEAM against CLIM; (b) SYS4 against CLIM; (c) SEAM against SYS4; and (d) difference
between SEAM against CLIM and SYS4 against CLIM. The dimension of the circles is proportional to
the number of days while the color scale refers to the number of weeks. The size and color of the
circles are therefore showing the same information, and are both added for clarity.
Mean relative error over all outlet points as a function of lead time in weeks
(a) for all starting dates of the forecasts and (b) for the starting dates close to the beginning of the
months. Negative values denote that the forecast is too wet in comparison with the CLIM run. The
SEAM (SYS4) forecast is in blue (black) where the solid line denotes the median and the filled area
(area between dotted lines) denote the 10 to 90th percentiles.
Since the two systems have different resolutions (Table )
the horizontal resolution was homogenized to the 5 km × 5 km equal-area grid
through a mass-conservative interpolation for precipitation and a bilinear
interpolation for temperature before it was used as input to the hydrological model in
EFAS. The mass-conservative interpolation summarizes the partial contribution
of the meteorological input fields onto the LISFLOOD grid. The time step was
reduced to daily by averaging (accumulating for precipitation and
evapotranspiration) the three hourly outputs of the ENS-ER and the six hourly
outputs of SYS4. Since the ENS-ER has a reduced hindcast (20 years) and
number of members (11), SEAM has the same number of members and hindcast
period. Note that in real-time mode, a full 51-member SEAM is possible. The
technical details of the forecast and the hindcast used in this experiment
are presented in Table . For simplicity, SYS4 and SEAM
will, from now on, refer to the full hydro-meteorological model chain and not
only the meteorological forcing.
Experimental setup
This study focuses on the performance of SYS4 and SEAM over the hindcasts of
the operational forecast. The hindcasts starting from 14 May 2015 (the first
available date with 11-member hindcast for ENS-ER) to 2 June 2016 were
used as input to the full EFAS modeling chain. As described above, the
hindcasts are the reforecasts over the previous 20 years and are produced for
each individual run of the ENS-ER. This provided 13 monthly starting dates
for SYS4 and 111 biweekly starting dates for SEAM with a corresponding
hindcast set covering all seasons over the previous 20-year period, each with
15 and 11 ensemble members, respectively (Fig. ). The output
was averaged to weekly means before the skill score analysis. Since the
starting dates of the SEAM and SYS4 were not always in sync (the starting
date of the SYS4 integrations are only sometimes on a Monday or Thursday), it
is impossible to do a completely like-for-like comparison since the
validation periods would be slightly different. However, this error will be
random and given the sample sizes (260 and 2220) it was not considered to have
a big impact on the results.
SEAM was validated against the runs with SYS4 to assess the added value of
the merged forecast. Further, both model systems were compared against a
climatological benchmark simulation (hereafter called CLIM). CLIM was
constructed by forcing the LISFLOOD with 15 randomly selected time series of
observed meteorological forcing from the period 1990–2014, excluding the
modeled year. CLIM has the advantage of having the same initial conditions as
SYS4 and SEAM hindcasts, but has no expected predictive skill beyond the
horizon of the initial conditions. The advantage of CLIM is that in theory it
has near-perfect reliability with regards to the WB runs since it is produced
with the same unbiased forcing data. It should, therefore, score better or
equal to the hindcasts as predictor on time ranges beyond their respective
limits of predictability.
Score metrics
The performance of the two forecast systems was compared against the WB run
at the 679 sub-basin outlets using deterministic and probabilistic scores. WB
is treated as a proxy for observations in the evaluation. The scores used
were the continuous ranked probability score (CRPS; ),
mean relative error (MRE) and forecast reliability through an attributes
diagram. All scores were calculated for SYS4 and SEAM over the hindcast
period. CRPS is a common tool to validate probabilistic forecasts and can
been seen as generalization of the mean absolute error to the probabilistic
realm of ensemble forecasts. It is defined as
Mean relative error for each of the outlet points for the SEAM forecast over the outlet
points for (a) week 2, (b) week 4 and (c) week 6. Red indicates where the forecast is too wet, and
blue where it is too dry. (d) Shows the difference in absolute error between SEAM and SYS4,
where blue (red) denotes points where SEAM has a smaller (larger) MAE than SYS4.
CRPS=1N∑t=1N∫-inf+infFt(x(n))-Ht(x(n)-x0)2dx,
where x(n) is the forecast at time step t of N number of forecasts and
x0 is the observed value (WB). The CRPS is the continuous extension of the
ranked probability score (RPS), where F(x) is the cumulative distribution
function (CDF) F(x)=p(X-x) and H(x-x0) is the Heaviside function,
which has the value 0 when x-x0<0 and 1 otherwise.
The CRPS compares the cumulative probability distribution of the discharge
forecasted by the ensemble forecast system to an observation. It is sensitive
to the mean forecast biases as well as the spread of the ensemble. Since the
SEAM has 11 members and SYS4 and CLIM have 15 members in the hindcast, the
CRPS are not directly comparable. showed that for two
ensemble distributions with different ensemble sizes, M and m, the
unbiased estimate for CRPSM based on CRPS calculated from the ensemble
size m is
CRPSM=CRPSm-M-m2Mmn∑t=1nΔt,
where
Δt=1m(m-1)∑i≠j|Xt,i-Xt,j|
is Gini's mean difference of ensemble members [Xt,1,…,Xt,m] at time
t. From the CRPS a skill score (CRPSS) can be derived by comparing CRPS of
the verified forecast against a reference forecast.
SSCRPS=1-CRPSfcCRPSrf
The mean relative error (MRE) was measured as the forecast bias in comparison
with WB normalized with WB, here defined as
MRE=1n∑t=1Nxo-x‾(n)xo,
where xo denotes the observed value (WB) and x‾(n) denotes the
forecasted ensemble mean at time t.
The reliability was assessed through an attributes diagram, where the
forecast probability of exceeding a certain threshold is compared with
observed frequencies . The forecast
reliability was evaluated for the 10, 50 and 90th percentiles of observed
discharge at each outlet.
Results and discussion
Overall forecast skill
Reliability diagram for SEAM (blue) and SYS4 (black) for week 4 for all outlet points.
Solid lines indicate the reliability for the median of observed discharge, the dashed (dotted)
lines the forecast reliability for the 10th (90th) percentiles of observed discharge.
Percentage of ensemble members predicting low-flow anomaly (< 97 %) on the river Rhine
north of Cologne for summer 2003. The two starting dates in August and September from SYS4 are
compared to the 17 starting dates of the seamless forecasting system. In two separate events the
discharge was recorded below the 97th percentile, event 1 on 17–27 August and event 2 on 18–28 September 2003.
The forecast skill gain provided by SEAM with respect to SYS4 is mostly
concentrated to the first 6 weeks (Fig. a) when the
forcing data are from the ENS-ER. The difference in CRPSS is 0.6 at week 1,
which then decreases to 0.1 by week 6. All points used in the validation
show a gain in skill up until week 3, then some points show a benefit of
using the SYS4 instead of SEAM. However, in some catchments there is skill up
further than 8 weeks. The overall better performance of SEAM with respect
to SYS4 is partly because of the use of a more recent model version and
partly because of the more frequent updates of the atmospheric and
hydrological initial conditions. It is possible to disentangle the relative
contributions between these two factors by only considering a reduced number
of starting dates for the SEAM forecast, i.e., dates that are the closest to
the SYS4 starting dates (Fig. b). This reduced
statistic provides a measure of the expected contribution of only
employing a newer model cycle in the first weeks while both simulations
benefits from the same hydrological initialization. In this case, the skill
gain in CRPS reduces to between 0 and 0.4 (median 0.2) against SYS4 for the
first week, reducing to neutral around week 4. Therefore the most relevant
gain comes from the more frequent initializations of the hydrological model.
To put these increments into context we also look at the improvement in skill
of the two systems (SYS4 and SEAM) against the CLIM benchmark forecast (Fig. c–d).
The gain from having improved initial conditions
in SEAM is similar in comparison with CLIM (Fig. c) as
with SYS4 (Fig. a) in the first week, but the skill
deteriorates quicker and the median CRPSS is negative after 5 weeks. Without
the increase in skill due to the advantage of the better initial conditions,
SEAM still shows a gain against the CLIM forecast with a CRPSS of 0.4 for the
first week, although the spread is quite wide (Fig. d).
Also, SYS4 shows an increase in skill against the CLIM forecast. Both
forecasts are less skillful than CLIM for most river points after week 4.
It can also be noted that SEAM has a higher spread than SYS4 on longer lead
times even though they are forced with the same data from day 47 and onwards.
An explanation can be that the ensembles from the two meteorological
forecasts are not matched member by member in terms of their relative
deviation from the mean, for example matching members from each distribution
according to their wetness. If two extreme driving forecasts from the two
meteorological forecasts are combined it can lead to members that are further
away from the ensemble mean than when only one driving forecast is used.
Geographical variation in forecast skill
The geographical distribution of skill gain provided by the SEAM and SYS4
prediction reveals a coherent picture with good scores against the CLIM run
over most of Europe (Fig. a–b). The gain in the
figure is expressed as a difference in the number of weeks into the forecast
needed for the CRPSS to drop below zero (i.e., there is no skill in the
forecast in comparison with CLIM), which gives an indication of the expected
time gain in terms of information provided by the forecast against the
reference forecast. Both SYS4 and SEAM are better than CLIM, and SEAM has
higher skill than SYS4 for most of Europe. There is a small negative effect
over the Alps, southeastern Europe and northern Finland (Fig. d). The performance of the operational EFAS in these
regions is generally poor, which is caused by the difficulty of having good
observations of precipitation in high-altitude stations and the atmospheric
models difficulty in resolving steep orography . The
snow accumulation and snowmelt are further divided into three elevation zones
within a grid in LISFLOOD to better account for orographic effects in
mountainous regions. However, this increase in sub-grid resolution is not
likely to be high enough to capture the snow variability during the snow
accumulation and snowmelt in mountainous regions. Further, precipitation
forecasts have documented biases in steep orography ().
Another interesting aspect to showcase is the relevance of more frequent
model version updates for the overall improvement of river discharge for all
stations in proximity to the western coasts. This can be attributed to recent
developments in the precipitation model scheme, for example a new diagnostic
closure introduced in the convection scheme and a new
parameterization of precipitation formation .
Bias and reliability
The relatively sharp decline in CRPSS can, to some extent, be explained by the
negative bias (too wet forecast) for both SEAM and SYS4 forecast (Fig. ). SEAM has lower bias than SYS4, also when the analysis
is confined to the first few weeks (Fig. b). The
slightly better bias in SEAM disappears quickly after the merge (week 7). The
bias of the forecast is not spatially consistent, it is generally larger
in western and central Europe (Fig. ). The figure shows the
bias for SEAM (a–c) but the pattern is similar for SYS4. SEAM has generally a
smaller bias than SYS4 (Fig. d). SYS4 has lower bias
south of the alps, where it also performs better than SEAM.
The reliability of a forecast in terms of its usefulness for decision making is important. A
reliable forecast can be trusted to predict the correct probability of
certain events, regardless of the accuracy. An unreliable forecast is in
practice of no use and can lead to poor decisions .
Both forecast systems are overconfident when it comes to predicting the
median flow, which can be attributed to an underestimation of the ensemble
spread (Fig. ). The results are comparable to a
previous study of 2 m temperature and precipitation over Europe with SYS4
. The reliability with regards to low flows (dashed
line, Fig. ) indicates an overprediction of low
flows, which can be explained by the wet bias of both systems causing
an overestimation of the low flows. SYS4 is performing better than SEAM in
this regard. The high flows are generally underestimated by both systems, but
SEAM performs slightly better than SYS4 (dotted line, Fig. ).
The skill of the forecasts from both systems could be potentially higher by performing a bias correction,
either on the atmospheric input and/or on the discharge.
However, in this paper we concentrate on the differences in skills provided by the various
configurations and no bias correction has been applied.
Added value of the seamless forecast
Even though the increase in the overall skill provided by SEAM in
comparison with SYS4 is noticeable, the justification for its use in an
operational context also depends on the actionable time gain in a response
situation. More frequent forecast updates could potentially be useful in
decision making. As an example, we analyze the predicted stream flow for the
river Rhine at a station just upstream of Cologne, Germany, during the European
heat wave in the summer of 2003. It was an exceptional meteorological event
which combined significant precipitation deficits with record-setting high
temperatures . At its peak in August, extremely low
discharge levels of rivers were reported in large parts of Europe. Economic
losses were huge in many primary economic sectors including transportation
. For several months, inland navigation was heavily impaired
and the major European transport routes in the Danube and Rhine basins ceased
completely .
Despite the fact that 2003 conditions were extreme from the meteorological
point of view, the upcoming deficit in precipitation and the high
temperatures were well predicted by the ECMWF seasonal systems operational at
that time (System-3; ). The good predictability of the
event is confirmed by the low discharge prediction provided by SYS4 at the
Rhine upstream of Cologne (Fig. ). More then 30 % of
the ensemble members forecast extreme low-flow conditions. In fact the
observed discharge confirms that the river flow on two separate occasions,
event 1 on August 17–27 and event 2 on 18–28 September 2003, went below the
third percentile of its climatological value for the season (Fig. ).
While most of SYS4 ensemble members mark the extreme
condition 3 to 4 weeks ahead, there is no information of the recovery
period observed between event 1 and 2 in the forecast starting the first
of August. SYS4 predicts a swift recovery back to normal conditions on the
forecast issued on 1 September. A more detailed picture of this intermediate
recovery is instead conveyed by the seamless system. Thanks to the more
frequent updates, the temporary increase in river flow is correctly picked up
giving a potential advantage of 2 to 3 weeks for planning actions. SYS4
does indicate the second low flow with a longer lead time than SEAM. However,
SYS4 misses the timing of the event.
Even if this was a good forecast for SYS4, the information it provides is
more informative (anomaly condition) than “actionable” . In
the above example, a decision maker would have to make a decision based on a
forecast that was issued 2.5 weeks earlier, which would inherently make the
decision more uncertain if you only had the seasonal forecast. With the
seamless system available, a decision maker would gain the same early
indication of a hazardous event and also have the benefit of frequent
updates. In this particular case, the SEAM forecast for the first event was
more unstable for some ensemble members, but in general the event was well
captured (Fig. ). The SEAM could also correctly capture
the recovery with higher water levels between the extreme low-flow events.
The onset of the second low period was correctly modeled by the SEAM system,
whereas the timing of the low flow was missed by SYS4. It should be said that
using other less extreme thresholds (< 10 and < 5 percentiles) even further
strengthened the case for using SEAM.
Conclusions
This study compared a set of hydrological hindcast experiments over the
European domain with two meteorological forcings: ECMWF's seasonal
forecasting system (SYS4) and a merged system of ECMWF extended-range
forecast and seasonal forecast system (SEAM). The latter showed a better
overall skill and lower bias over most areas in Europe with lead times up to
7 weeks. This increase in skill could be attributed to better initial
conditions of the hydrological and meteorological model as well as a better
atmospheric model version in SEAM. In some areas, particularly in the Alps
and northern Finland, the seasonal forecast outperformed the merged forecast.
However, in these areas the predictability of the hydrological model is
generally poor, which makes these results quite uncertain. Given that the
skill in the sub-seasonal range over Europe is in the range of the
extended-range ensemble forecast, this motivates us to use the ENS-ER instead of
SYS4 for hydrometeorological predictions.
Still, there is an added benefit of using a seamless forecast over the
extended range due to the extension of the forecast horizon for the early
detection of upcoming anomalous conditions. Indeed, as an example, this study
also highlighted the potential for the use of a sub-seasonal to seasonal
forecast in the case of an extreme low-flow situation in the river Rhine. The
higher frequency and skill of SEAM has the advantage of being a more
“actionable” forecast than seasonal forecasts, given that a decision maker
would be able to make use of the extra information. Care should be taken when
using the forecasts in decision making since the reliability over Europe is
“marginally useful” . It is therefore important to
assess the reliability and skill of SEAM at the location it is to be
implemented over the season of interest.
Future work with the seamless forecasting system is to further explore the
limits of predictability, reliability and bias to assess the strengths and
limitations of the current setup. The assumption that the forecasts can be
randomly concatenated would also need to be tested against a system where the
forecasts are matched according to their respective climatology. Bias
correction of the forecasts might be a necessity, and the advantage of the
extended-range and seasonal forecasts from ECMWF is that the availability of
hindcasts enables just that.