Applications of ensemble forecasting products for medium-range forecasts
Warren Tennant
Research Group for Seasonal Climate Studies - South African Weather Bureau
Dynamical extended-range forecasting at the SAWB
Introduction
The South African Weather Bureau (SAWB) operates two supercomputers, a J-90 (since
September 1996) and SV-1 (since November 1999). This computing power permits running a
general circulation model (GCM) for extended-range applications. Two GCMs used are the T30
version of the Center for Ocean-Land-Atmosphere Studies (COLA) GCM and the T126 and T62
versions of the National Centers for Environmental Prediction (NCEP) GCM, implemented locally
as the Global Spectral Model (GSM).
The GSM model (Sela, 1980) was developed at NCEP for global medium-range forecasts.
The GSM T126 version is used at the SAWB for twice-daily forecasts up to168 hours ahead and
the GSM T62 version is used for the ensemble production suite of14-day forecasts. Prognostic
variables are represented by spherical harmonics of legendre polynomials with triangular
truncation at wave number 62. This corresponds to a horizontal grid of 192 by 94 points, about
200 km. The vertical coordinate is the sigma parameter which is the pressure at a level normalized
by the surface pressure below that point. There are 28 unevenly spaced sigma levels. Physical
processes included in the model are deep and shallow convection, large-scale precipitation,
radiation, surface physics, vertical diffusion and gravity wave drag.
The COLA T30 model is a spectral model with triangular truncation at wavenumber 30.
This corresponds to a horizontal Gaussian grid of 96 by 48 points, roughly 400 km resolution.
Physical processes included in this GCM are similar to those of the GSM T62 model. A simple
biosphere model is also included to enable the GCM to be used for climatological studies. Data
processed in this part of the model are deep soil temperature, ground temperature, canopy
temperature, soil moisture, liquid water storage, latest computed precipitation, roughness,
maximum mixing length and sea-ice temperature (Kirtman et al., 1997). This model is used for
monthly and seasonal forecasts.
Two week forecasts
Classical predictability theory suggests that skilful daily forecasts may be realized up to two
weeks ahead (Lorenz, 1969). However, model integrations from slightly differing initial conditions
will diverge rapidly and produce different forecasts. To address this problem ensemble forecasting
has been introduced to produce a probability distribution of likely outcomes as opposed to a
deterministic forecast (Hoffman and Kalnay, 1983). The SAWB has adopted the two-week
forecasting system from NCEP where a 16-member ensemble is created daily using a breeding of
perturbations method (Toth and Kalnay, 1993). Combining the bred ensembles from successive
initial times using the lagged-average forecasting technique the ensemble suite can be increased
to 43 members. Updated two-week forecasts from this system become available every day. This
system is only used for forecasts from day 8 to 14. The first seven days of the Bureau's forecast is
produced using the 12Z control forecast from ECMWF with the GSM T126 as backup. Initial
conditions for these model integrations are obtained in real-time directly from NCEP relieving the
SAWB from running a global data assimilation system locally.
Ensemble techniques facilitate the compilation of various forecast products. These include
probability calculations and cluster analysis. The ensemble of forecast temperature, wind and
cloud cover fields are clustered into three groups independently at each grid point according to the
sequence of changes through the forecast period. An average of the largest populated cluster is
then used to produce the final forecast. A simple ensemble mean smoothes out the data too
much.
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Rainfall is mapped as a probability of the amount exceeding a threshold value by simply
calculating the proportion of ensemble members that project the event.
Weighted forecast maximum and minimum temperatures from the largest cluster at each
model grid-point are used to calculate regional values. Local conditions (e.g. coast and
topography) influence temperatures strongly and these may not be adequately simulated
by the forecast model. To overcome this problem model forecast temperature changes
from day to day are added to observed temperatures at the initial time of the forecast.
Wind speeds are determined from an average of the cluster members and the dominant
wind-components in the cluster are taken as the wind direction.
Model cloud cover is output as a percentage for three levels (low, middle and high).
Forecast cloud cover is given in three categories, viz. Clear (< 25% cover),partly cloudy
(25% to 75%) and cloudy (> 75%), based on the maximum cloud cover on the three model
levels.
Monthly forecasts
Monthly forecasts based on the COLA T30L18 GCM have been produced by the SAWB
since 1995 (Tennant, 1999). The GCM is initialized nine times at six-hourly intervals from 00Z on
Friday to 00Z on Sunday every week for a 30-day forecast. Atmospheric initial conditions are
created from T126L28 sigma analysis files received six-hourly from the National Centers for
Environmental Prediction (NCEP). Lower boundary conditions consist of initial observed seasurface temperature anomalies persisted for the month of the forecast and climatological snow
and ice cover fields. A weighted average, according to forecast length, is calculated from the nine
ensemble members. Prognostic fields of sea-level pressure and 500 hPa heights are biascorrected at each grid-point based on monthly-dependent errors of retro-active model forecasts
between 1979 and 1995. Forecast anomalies of surface temperature and outgoing long-wave
radiation are determined using the monthly-dependent model climate of the 17 years mentioned
above. Rainfall forecasts are plotted as a percentage of normal to overcome the problem of a high
GCM rainfall bias.
Seasonal forecasts
A cheaper alternative to fully coupled GCMs has been adopted by the RGSCS. This multitiered system consists of four tiers. The first is predicting sea-surface temperatures using a
statistical method, next the GCM is integrated using the predicted SSTs as a lower boundary
forcing, then large-scale circulation fields forecast by the GCM are downscaled to regional rainfall
using a statistical method and finally forecast guidance from various models are combined to
produce a probability forecast. More information on this project can be found at
http://www.weathersa.co.za/rgscs/products/derf.htm.
Probabilistic versus deterministic (forecaster view)
Forecasters are in the most fortunate position regarding the understanding of probabilistic
forecasts because they have been trained to forecast in terms of probabilities. They understand
that the weather has a number of possible outcomes and couple a probability to each event. It is
known that ensemble forecasting techniques are useful in determining these probabilities.
However, ensemble forecasting remains foreign to many forecasters who are comfortable to use
output from a single forecast model and are not aware of how sensitive GCMs are to changes in
initial conditions. Perhaps, in this light, ensemble forecasting techniques are not being used as
widely as they ought to be. Indeed, at the South African Weather Bureau forecasts up to seven
days ahead are done using a single GCM forecast from ECMWF and the ensemble forecasting
technique is only applied for 8 to 14 day forecasts.
Probabilistic forecasts have many positive aspects. Rainfall probability maps are popular
on websites for users who want a general picture of rainfall possibilities for the medium term. Such
maps are easy to create and can be calculated and disseminated automatically. Probabilities are
also useful in alleviating the uncertainty regarding the timing of a significant weather event.
Forecasts sometimes are seen as incorrect because of an unexpected delay in the arrival of a
weather event such as a cold-front. A probability forecast can state when it is most likely for the
event to arrive. The position and intensity of weather systems can also be tracked easier in a
probability sense using spaghetti diagrams. For example, the probability of a cut-off low system
developing in a particular area can be determined from the spaghetti diagram. These diagrams
are also useful in determining at a glance what the predictability of the weather is for a particular
period. The decrease in skill with lead time becomes clearly visible.
Ensemble forecasts also present a number of difficulties as seen from a forecaster point of
view. Rainfall events which last for a period of less than a day, particularly during dry seasons,
may be forecast for a period of several days with low probability. This can occur when the timing of
the event is spread out across the ensemble. The probability may be too low to pass the minimum
threshold of giving a deterministic rain forecast for a specific location with the result that the event
is not forecast at all. The corollary of this is that areas with a high number of rain days (wet
season) have a medium to high probability of rain forecast nearly every day and it becomes
difficult to establish which are non-rain days. Similar problems are encountered with temperature
forecasts where averaging over the ensemble of forecasts smoothes temperature trends away.
This problem increases with forecast lead time.
Medium-range forecast output is usually presented on a 2.5 x 2.5 degree grid which is too
course to include small-scale effects such as coastlines and escarpments etc. In South Africa
these effects are quite noticeable. The winter rainfall areas in the southwest of the country
experience enhanced rainfall owing to topographical effects but this is not captured adequately on
a medium-range forecast model.
Minimum and maximum temperature forecasts for individual stations can be calibrated
against observed temperatures at the start of the forecast period to overcome model bias and
coarse model resolution. A caveat here is that the ensemble of forecasts, which include model
integrations from different initial times, may not capture the timing of an abrupt cooling exactly and
the calibration will be unsuccessful. For example, if the model forecasts give a 10oC cooling at a
station 12 hours later than observed, the cooling could be included twice and the forecast would
be way too cold. The reverse is also possible with a dramatic cooling being missed in the forecast.
Quality control is recommended when compiling these forecasts to avoid unrealistic temperature
forecasts being issued. Experience has shown that a human forecaster with his vast experience of
local conditions is best for this task.
One of the most difficult parameters to forecast is wind direction and speed. When there is
a large spread in the ensemble the vector wind may be reduced to zero. At the SAWB we
calculate the average wind speed as a scalar and set the direction as the mode of the ensemble of
directions. There has only been moderate success with wind forecasts.
Individual components of the forecast can be generated automatically as probabilities from
the ensemble. However, as with temperature calibration human intervention is necessary to
ensure that the overall forecast is consistent. For example a if a cold temperature is forecast but
an offshore wind with adiabatic warming (berg wind) is forecast at the same time the most basic
user will question the forecast. Inconsistencies like this can be aggravated if the parameters in the
ensemble are clustered separately.
Probabilistic versus deterministic (public user view)
For day to day forecasts the general public is usually interested in a deterministic forecast
of the most likely weather conditions. Probabilities can be confusing and be misinterpreted. For
instance, ensemble products may give a probability of an event of rainfall to be 15% and the
coverage of the rain could be 20%. This amounts to an actual chance of rain at the user's location
to be 3%. However, the user may still think there is a 20% chance that he will receive rain.
Alternatively he may convert the probability forecast into a deterministic one and decide rain is
definitely out. A further danger of probability forecasts is the problem, mentioned earlier, where
rainfall probabilities of a short-lived event are spread over several days. This can give the
impression that it will rain continuously for a number of days instead of for a brief period at some
point during the week.
Public users prefer forecasts that are site-specific and not for a region. Furthermore, they
prefer the forecast written generic terms without having to decipher probability maps. This adds to
the load of the human forecaster because this type of information is difficult to generate
automatically by computer.
Probabilistic versus deterministic (professional user view)
Professional users have a definite need for probabilistic forecasts. Their applications are
usually such that they can change their operations based on the probability of a range of weather
events occurring. Generally medium term forecasts are used for tactical decisions such as taking
precautions against frost. Typical queries from professional users concern the start or end of
large-scale rain events, dry periods and likelihood of frost. These users are often in a position to
pay for forecast information that is tailor-made for their specific application. This can constitute a
significant part of the income of a weather service.
Summary
Probabilistic forecasts are certainly valuable and offer a greater amount of information than
deterministic ones. Professional users are able to put this information to good use but generally
public users require a simple deterministic forecast. A number of difficulties in producing
deterministic forecasts using probabilistic ensemble forecast output have been discussed. This
leads to the importance of quality control of medium-range forecasts to remain credible. In South
Africa medium-term forecasts are one of the most popular products and this warrants additional
resources been allocated to this important service.
References
Hoffman, R.N. and E. Kalnay, 1983: Lagged Average Forecasting, an Alternative to Monte Carlo
Forecasting. Tellus, 35A, 100 - 118.
Kirtman, B.P., J. Shukla, B. Huang, Z. Zhu and E.K. Schneider, 1997. Multi-seasonal Predictions
with a Coupled Tropical Ocean-Global Atmosphere System. Mon. Wea.Rev., 125, 789 808.
Lorenz, E.N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J.
Atmos. Sci., 26, 636 - 646.
Sela, J., 1980: Spectral modelling at the National Meteorological Center. Mon. Wea. Rev.,108, 12
Tennant, W.J., 1999, “Numerical forecasting of monthly climate over South Africa”, Int. J.
Climatol., 19, 1319-1336.
Toth, Z. and E. Kalnay, 1993: Ensemble forecasting at NMC: The Generation of Perturbations.
Bull. Amer. Meteorol. Soc., 116, 2522 -2526.