Dear Dr Nakazawa,
Thank you for your continued consideration. I would like to try and provide further
information to your questions below (questions in italics):
1. Objectives of the project
I understand that the previous study shows the usefulness of surface relative humidity
and temperature. But as you may know that the TIGGE database is basically ensemble
forecast data, not deterministic forecast data. Would you please tell us a bit more how
do you utilize the TIGGE ensemble forecast data?
Our statistical analysis was carried out using reanalysis (deterministic) products to
define the relationships between weather variables and meningitis likelihood. In
particular, these relationships quantify the probability of exceeding 0, 5, 10, and 15
reported cases per 100,000 individuals in districts across the meningitis belt, as a
function of changes in weather. The solid line in the right panel in Figure 1, in
particular, shows how the likelihood (y-axis) of exceeding 10cases/100,000 changes
strictly as a function of relative humidity (x-axis); the solid horizontal line shows the
“climatological” likelihood, of about 2% if one did not have weather information
(note that other weather variables also have an impact, not shown here).
With these statistical relationships, actual forecasts of meningitis likelihood are
produced by running individual TIGGE weather forecast ensemble members into
these meningitis statistical models. For each day, the TIGGE forecasts are temporally
averaged into 1week- and 2week-ahead forecasts (from 6hr – 7days, and 7-days –
14days, respectively). Due to biological and administrative lags, a 1week-ahead
weather forecast translates into a 3week-ahead meningitis forecast. In Figure 2 we
show an example of using only a 1week-ahead ECMWF ensemble mean relative
humidity (RH) forecast initialized on March 27th 2011 00Z to produce a 3week
ahead meningitis risk of exceeding 10cases/100,000 across the meningitis belt
(bottom panel). The benefits of utilizing the full TIGGE set of ensembles is that, after
calibration, we have a set of individual ensembles that represent equally-likely
weather scenarios and thus a range of meningitis outcomes that thus is an indicator
of forecast uncertainty.
In addition to producing meningitis belt-wide forecasts of exceeding thresholds of
cases/100,000 1week, 2weeks, 3weeks, and 4weeks ahead, we also produce end-ofseason meningitis forecasts. Here, we forecast the date beyond which the risks of an
outbreak are lower than the climatological risk, and remain lower over a given
number of weeks (we currently use 6weeks). Here, we also explicitly use the
implied uncertainty information contained in the range of TIGGE ensemble
forecasts. This is accomplished by calculating the accumulative probability of not
reaching epidemic thresholds for each of the next 6 weeks, done by randomlysampling (e.g. bootstrapping) the calibrated TIGGE ensemble-based meningitis
outbreak likelihoods 1week to 4weeks in advance, and then historical reanalysis for
the last 2 weeks (for a total of 6weeks), generating a range of possible end-of-season
dates for each location. As a result, we continually monitor and update the date at
which point outbreak risk is sufficiently low enough that health managers and
vaccine dissemination programs could assume regional safety from a possible
outbreak.
2. Relation to the commercial grants/sponsors
I found that you will develop a prototype forecast system, which does not use the
TIGGE data. If so, I suspect what is the value at this time to utilize the TIGGE data.
We apologize for not being more clear in our response. In point of fact, our
prototype meningitis forecast system explicitly uses the TIGGE weather forecast
ensembles to provide both the most-likely scenario and the associated degree of
uncertainty in meningitis outbreak likelihoods and expected end-of-season dates, as
we attempted to explain above in 1.
3. Reason to request relaxation of the 48h delay
It is not clear to me why you need relaxation of the 48h delay. You mention in the
memo, you would like to get "most valuable, accurate, and timely input data". If there
were such kind of data available, I also would like to get such data. The memo does not
answer the question why you request relaxation of the 48h delay. As you may know,
the TIGGE data is open from 2006 or 2007 at three archiving centers (ECMWF, NCEP
and CMA) to everyone for research purposes. I want to know why the current TIGGE
data is not sufficient to your project. My point is that you may still utilize the TIGGE
database for your purposes as it is, even though the data is in delayed mode. I would
like to ask you if you have experienced the TIGGE ensemble forecast data in delayed
mode in your project. If not, then I strongly recommend to use the current datasets in
your project. I am almost sure that the results would be similar between the near-real
time products and the current delayed data.
We request the relaxation of the 48h delay primarily because our pilot program is
designed to provide to health care and monitoring officials the most up-to-date
probabilities of exceeding meningitis outbreak thresholds (and end-of-season dates)
across the meningitis belt during the upcoming dry season (nominal start time in
November-December). Although we believe our information would still have utility
if it were 2day delayed in its release, we also believe our information could show
even more potential utility if these delays were reduced, and hopefully present an
even stronger case for the utility of ensemble medium-range weather products in
this particular application.
Because in general, ensemble weather forecast information needs to be calibrated,
we use the past year’s ensemble data from 8 of the TIGGE-reporting weather centers
as representative hindcasts to train our statistical post-processing algorithms
(based on quantile regression). The post-processing is applied individually for each
forecast lead-time (6hr, 12hr, …, 336hr), using reanalysis as “truth”. We show an
example where we have used forecast information from March 2011 from ECMWF
and the NCAR archive in Figure 2 below.
4. Which data are requested
You do not mention in the memo that you need ensemble forecast data. The question is
that how you extract "valuable, accurate" information from the ensemble data. This is
the question, which we in WMO, are working very hard. It is on-going activity in the
TIGGE Working Group.
As we tried to discuss above in 1., we utilize the ensemble forecast data to generate
the most-likely scenario of meningitis outbreak over the meningitis belt, as well as
provide the range of uncertainty in these forecasts. In, particular, this is explicitly
represented in the way we calculate our end-of-meningitis-outbreak season dates,
where we bootstrap over our calibrated TIGGE-generated ensemble set to produce a
range of possible end-of-season dates, which are continually refined and improved
as the meningitis season progresses.
Figure 1: World Health Organization districts across part of the meningitis belt
shown in the left-most panel, with the meningitis epidemic (here, as defined by 10
cases/100,000) likelihood dependence on relative humidity shown in the rightmost
panel.
Figure 2: Top panel shows the one-week ahead average relative humidity (RH)
ensemble mean forecast over the meningitis belt, initialized on March 27, 2011. The
bottom panel shows how this RH forecast converts to a 3-week ahead meningitis
forecast across the belt, represented as probability of exceeding 10 cases/100,000
reported over that week.