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Uncertainty-Chain Management: Bridging the Gap between Forecasts and Well-

being

Yoshiyuki Tomiyama Weather Environment Education Center, Tokyo, Japan

Ryozo Tatehira Weather Environment Education Center, Tokyo, Japan

____________________ Corresponding author address: Yoshiyuki Tomiyama, Weather Environment Education Center, 3-17 Kandanishikicho, Tokyo, Japan E-mail: rakufuh@yahoo.co.jp

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Abstract

The value of forecasts is not realized in well-being automatically. Despite years of

discussion, the efforts to bridge the gap between forecasts and well-being has not yet

born fruit. This situation is consistent with the fact that the private sector of the “Weather

and Climate Enterprise (WCE)” is still limited in revenue even in US and Japan. In this

study we will propose a promising approach to bridge the gap. Our approach is to

consider the gap as “uncertainty-chain”, which consists of four links of uncertainty, that

is, uncertainty in forecasts, in communication, in forecast use and in decision-making.

These four links of uncertainty-chain should be comprehensively managed, and we call

such a management the “Uncertainty-Chain Management (UCM)”. We illustrate our idea

by referring three cases of decision-making performed in practice in Japan and US. It is

expected that risk-takers will come to need the aid in their UCM. We apply the word

“UCM-service” to the service providing risk-takers with the aid. The UCM-service will

be a new industry to bridge the gap between forecasts and well-being. We will also

suggest changes in the WCE, which will be provoked in connection with the UCM-

service.

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1. Introduction

The anticipated impacts of global warming intensify a feeling of uneasy

prevailing over the “Weather and Climate Enterprise (WCE).” The WCE is defined as all

sectors and parties engaged in generating and communicating weather and climate

forecasts [National Research Council (NRC) 2006]. The feeling of uneasy seems to be

caused from the gap between forecasts and well-being.

Significant gaps remain between the weather forecast information available and

its use in decision-making to reduce losses from disaster or to make economy efficient

(e.g., Mass 2005; Morss et al. 2008). While Japan is one of the countries where various

weather information is easily available, emergency managements are often carried

without scientific information (Ushiyama, et al. 2003).

The fact that the private sector of the WCE is still limited in revenue even in US

and Japan indicates another aspect of the gap. The sum of US public and private sector

meteorology expenditures, including all operations and research, is about $5.1 billion

(Lazo, et al. 2009). This is one-thousandth of the annual income of some $3 trillion,

which has any relation to weather and climate risk, in US (Dutton 2002). The US

expenditure to the private sector meteorology, i.e., the revenue of private sector is

estimated between $1.65 billion and $1.8 billion (Spiegler 2007) of $5.1 billion. The

private sector of Japan has sold ¥30 billion (ca. $0.3 billion) a year in the last decade

without any growth [the Japan Meteorological Agency (JMA) 2006]. Spiegler’s figure

includes broader items such as weather instrumentation than the figure of Japan. The

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limited revenue of the private sector is considered not only a result of the gap but also a

reason of the gap.

While the potential societal value of recent weather forecasts is substantial, the

realization of that potential is not automatic (Pielke and Carbone 2002). It was clearly

shown by the experience of Hurricane Katrina in 2005, which ranks as the costliest and

one of the deadliest Atlantic hurricanes in history (e.g., McTaggart-Cowan et al. 2007).

Rosenfeldt (2006) wrote on this hurricane “what makes Katrina particularly perplexing is

that forecasting accuracy achieved such a triumph”. A forecast triumph is not sufficient to

prevent disaster losses because of the gap between forecasts and well-being. Like the

natural events to be predicted are uncertain, this gap is equally, or rather more uncertain.

Despite years of discussion, the efforts to bridge the gap has not yet born fruit

(e.g., Pielke and Carbone 2002; Morss, et al. 2008). To manage the weather and climate

risks, it is needed to shed light on the uncertainties between forecasts and well-being. The

approach of this study is to consider the gap as the “uncertainty-chain”, which consists of

four links of uncertainty, that is, those in forecasts, in communication, in forecast use and

in decision-making. The forecast value will not be realized in well-being unless four links

of uncertainty-chain are comprehensively managed. So, the “Uncertainty-Chain

Management (UCM)” is to be considered as the essence of risk management. This study

aims to show the feasibility and outline of the UCM. We illustrate our idea by referring

three cases of decision-making carried out in Japan and US, where the background of

affluent information and easy accessibility are shared.

We recognize four links of the uncertain-chain in section 2. In section 3, we will

explore the nature of each link, and try to show the way to manage it. The aids to risk-

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takers for their UCM will come to be needed. Section 4 proposes a service providing

these aids under the name of the “UCM-service”, and illustrates its expected role. We

conclude by section 5 with a summary and an outlook of the future WCE.

2. Uncertainty-chain

The report Completing the Forecast [The National Research Council (NRC)

2006] recommended generation, communication and use of uncertainty information to

the Weather and Climate Enterprise (WCE), stating that no forecast is complete without a

description of its uncertainty. A paradigm shift from deterministic forecasts to

probabilistic forecasts in the recommendation is an indispensable step to the risk

management. However, currently only a small fraction of forecasts is expressed

probabilistically. The WCE seems to hesitate of the paradigm shift. This is in part due to

lingering questions about how well the general public will understand probabilistic

forecasts (Joslyn, et al 2009).

However, the heart of the matter is a fact that sources of uncertainty to be

managed are not only forecasts. It is critical to the weather and climate risk to manage

other sources of uncertainty between forecasts and well-being together with forecast

uncertainty. Scientific uncertainty such as forecast uncertainty is only one of many

components of the uncertainties in risk managements (e.g., Morss, et al. 2005, NRC

2006).

We try to recognize four links of uncertainty between forecasts and well-being

(TABLE 1). First, forecasts aren’t free from error (uncertainty), some part of which is due

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to the predictability limit of the chaotic character of the atmosphere. Second, the

communication may be uncertain due to poor infrastructure. Besides, the way of

information flow is another source of uncertainty, as we will exemplify some cases in US

and Japan later. Third, there usually are some differences between needed information

and provided forecasts. Fourth, decision problems are rarely well-specified. There will

always be the dark and tangled stretches in the decision-making process to describe it in a

well-specified manner (Hammond 1996).

The above four uncertainties can be classified into two types, scientific and non-

scientific ones. The uncertainties other than forecast uncertainty belong to the latter type,

which includes vagueness, ambiguity and arbitrariness. It is not known whether they are

reducible or not, because they have never been treated. They must be shed light of

science on, at the beginning.

It is essential that these uncertainties constitute a chain, which we call the

uncertainty-chain. Therefore, not only management of individual uncertainty but also

comprehensive management of the uncertainty-chain is needed, which we will refer to as

the Uncertainty-Chain Management (UCM). The UCM is a management to improve

decision-making by controlling uncertainties in forecasts, in communication, in forecast

use and in decision-making, through understanding them as the chain. Risk management

will not be effective unless all links of the uncertainty-chain are managed. This may be

the main reason why the accurate forecasts alone are not sufficient to promote well-being.

3. Uncertainty management

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To explore the nature of the uncertainty-chain we examined three cases of

decision-making (TABLE 2). The first case is a sediment disaster from heavy rain on 23

July 2003 in Kyushu district, which is referred to as “Kyushu 2003” hereafter. There are

excellent documents on this case (the Disaster Prevention and Human Renovation

Institute (DRHRI) 2004; Takahashi et al. 2005). The second case is winter season snow

removal on the New York Thruway, and is referred to as “New York Thruway”. An

outstanding study by Stewart et al. (2004) instructs us much. And the third case is a local

flash flood accident from thunderstorm on 5 August 2008 in Tokyo, which is referred to

as “Tokyo 2008”. A report of the commission on the accident [The Tokyo Metropolitan

Bureau of Sewerage (TMBS) 2008] is available on this case. In the case of Kyushu 2003,

we focus on the decision-making of evacuation counsel made by local governments. In

the case of Tokyo 2008, we have to examine what could be done in ten or twenty minutes

between the beginning of rain and the accident. While Kyushu 2003 and Tokyo 2008 are

the cases of risk management at risk from the extreme events, New York Thruway is the

decision-making as routine practice.

a. Decision-making

DECISION DESCRIPTION

Although various dimensions have been proposed to specify individual decision

problem (e.g., Wilks 1997; NRC 2006), we address the following five dimensions: 1) the

risk-taker; 2) the risk; 3) the hazard; 4) the action alternative; and 5) the decision trade-

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off. We call these five the “basic dimensions” of decision problem. And we call

specifying these dimensions definitely the “decision description.”

The key factor of a decision problem is the person responsible to the risk, i.e., the

risk-taker. We define the risk as possibility of negative consequences for the risk-taker to

be concerned. The decision-maker is assumed to be the same as the risk-taker, or

assumed to act for the risk-taker. The risk-taker of each case is cited at the top of TABLE

2.

BASIC DIMENSIONS

In this study, we focus on weather- and climate-induced events that directly cause

the negative consequences (losses) to the risk-taker. These events are referred to as the

“hazard”. Therefore, the hazard is identified depending on the vulnerability of the risk-

taker. Here is an epistemological gap between weather and climate events and hazards.

The loss is a realized risk, and can be avoided or reduced by appropriate decision making.

The losses are always societal events. In a decision-making, multiple evaluation criteria

must be applied, and trade-offs between competing or conflicting criteria may be

necessary (Stewart 1997).

TABLE 2 shows an example of decision description for our three cases. In the case

of Kyushu 2003, we concentrate the risk from debris flow, and the decision alternative as

whether to announce the evacuation counsel or not among several decision points. In

Japan, the Basic Law for Disaster Prevention obliges the chief of municipality to make

decisions of evacuation of residents. Decision trade-off consists of losses due to the false

negative and needless evacuation in vain in the case of false positive. A false positive

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means erroneous decision to act when no event occurred, and a false negative also means

erroneous decision but not act when event occurred. The decision of false positive might

reduce credibility to the evacuation counsel.

In the case of New York Thruway, the risk is the chances of traffic flow disorder,

and the hazard is snowfall and freezing on road surface. Among several decision points,

we will focus on the decision-making to call crews and trucks for snow fighting. Decision

trade-off consists of realized risk due to the false negative and the costs incurred by the

false positive. The hazard in the case of Tokyo 2008 is flash flood in the sewer pipe.

Decision trade-off consists of realized risk due to the false negative and the inefficiency

incurred by the suspension of work by the false positive.

b. Translation

On the relation between forecast and decision-making Johnson and Holt (1997)

stated. “From the brief survey of applied valuation studies, it is apparent that weather

information is rarely used in the form observed, reported, or even necessarily intended.”

This may imply two meanings. One is that forecast specifications such as spatiotemporal

division and data form do not meet to the user’s need. And the other is that weather

information is rarely good for knowing the hazards, which directly cause the negative

consequences. The latter is the main source of uncertainty in the relation between forecast

and decision-making. Stewart, et al. (2004) documented an example that New York

Thruway supervisors must, implicitly or explicitly, make their own forecasts of a critical

variable—pavement temperature, which is directly associated to the decision-making.

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HAZARD INFORMATION

We propose a new type of information relating to the hazard, which is called the

“hazard information”. Since individual risk-taker is interested in different hazards, the

hazard information should be specific to individual risk-taker. The hazard information

has to consist of two components, i.e., “hazard predictions” and “hazard alerts”. Hazard

alerts are the information of alert-form to take yes/no value, which corresponds to go/no-

go decision. Hazard predictions are the information of probabilistic form, which are to be

converted to the hazard alert.

TRANSLATION

For example, the meteorological event as the primary cause of Tokyo 2008 was a

thunderstorm, and the hazard was a flash flood at a specific location of the sewer pipe. In

order to use in decision-making, weather forecasts must converted to hazard information.

We call it the “translation”.

The thunderstorm might result in the runoff depending on land surface processes,

after then, the runoff might result in the flood depending on characteristics of the river or

the sewer. The causes in each stage also constitute a chain, so we refer to it as the “cause-

chain”. While the Uncertainty-Chain Management (UCM) is comprehensive management

of uncertainties including social dimensions, the translations are associated with the

cause-chain in the domain of natural sciences. An extra link to the cause-chain usually

increases in uncertainty. The cause-chain between a thunderstorm and a flash flood on the

sewer is longer than that between the water level of upper stream and the flash flood.

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Because of the shorter cause-chain, the information of water level will reduce the

uncertainty in comparison to that of heavy rain forecast, even though the lead time will

become much shorter.

INFORMED DECISION

TABLE 3 lists the information used in practice or ruled to use in decision-makings.

In the case of Kyushu 2003, the emergency management was ruled to make decision for

evacuation counsel, by using detailed subjective analysis of raingauge measurements

(DRHRI 2004). This rule seems to have no real-world applicability in the incident

management, and was ignored at that time. Today, the sediment early warning (SEW) can

be used as the hazard information. The SEW is the information “to support local

governors to be able to make timely and appropriate evacuation decisions when sediment

disaster is anticipated” (Commission on the Sediment Disaster Early Warning 2003), and

is disseminated jointly from the prefectural office and the local observatory of Japan

Meteorological Agency (JMA). The main information used in the SEW is the soil water

index (Okada, et al. 2001), which is calculated from the precipitation analysis based on

raingauges and radar observations (Makihara 2000).

Since no hazard information was available in both cases of New York Thruway

and Tokyo 2008, decision-makers were able to use only some proxies in lieu of it, when

needed. In the case of Tokyo 2008, it was ruled to use the heavy rain warning at that time

(TMBS 2008). This means to substitute the heavy rain warning for the hazard

information, in order to know the flash flood at a specific location of the sewer pipe. We

consider these substitutions a kind of the translation. Decision-makers of New York

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Thruway use empirically various information including the National Weather Service

(NWS) zone forecast, radar display, reports from other thruway facilities and observation

(Stewart et al. 2004).

IMPROVED TRANSLATION

The improvement in translation will be expected by the following three

approaches: 1) to improve quality of input for the translation; 2) to use inputs with shorter

cause-chain; 3) to improve translation by integrating knowledge and information of

multi-field. The study by Stewart et al. (2004) aimed at the effect of improved

Quantitative Precipitation Forecasts (QPF) on the decision process. In the case of Tokyo

2008, the civil engineering company contracting with the TMBS has ruled to use

supplementarily on-site watch of water level. It means to use an input of shorter cause-

chain.

According to the recommendation of the commission on the accident, the TMBS

has ruled to make decision based on the heavy rain advisory (not only warning) or the

observation of “one drop of rain” at the site, after then (TMBS 2008). Using the heavy

rain advisory will result in some decrease in false negative (miss) and much increase in

false positive besides. Using the observation of “one drop of rain” will result in far much

increase in false positive without preventing miss completely. Because, flash flood may

occur by the heavy rain on other sites. On the other hand, the increase in false positive

will compel the risk-taker to suspend the work so frequently that the work might be

retarded. Improved hazard alert in this case could be made, for example, as follows.

Among the information easily available, the risk-taker prepares: 1) the heavy rain

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warning; 2) the thunderstorm advisory; and 3) eye-watching of clouds and lightning in

the direction of upstream. Combining 2) and 3) by “and”, then combining it with 1) by

“or”, a hazard alert could be made. It will drastically reduce false positives compared to

the TMBS new rule.

The alert could be free from false negatives if combined with the watch of on-the-

spot water level by “or”, though the lead time may be reduced to 5 minutes when short.

However, in order to prevent the accident by using such information of very short lead

time, detailed rules and drills will be needed.

HAZARD TRANSLATOR

The integrated system for the translation, which projects hazard information from

the information on its causes, is referred to as “hazard translator”. FIG. 1 shows the

outline for the case of three input information, of which two are processed through two

layers. In general, inputs will ranges over variety of disciplines with diverse cause-chain

and uncertainty to the hazard.

The main inputs to hazard translators are weather and climate forecasts. These

forecasts are required to have interfaces suitable to translation procedures in two aspects:

1) the specification; and 2) the uncertainty expression. The specification of forecasts

represents a set of forms, which include forecast period, spatial resolution, temporal

division, etc. Only forecasts with acceptable uncertainty at relevant lead time can be used

as the input of hazard translator. Note that the forecasts without uncertainty expression

are indistinguishable to the forecasts with poor accuracy.

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ALERT

The hazard prediction in probabilistic form can favorably be transformed into the

hazard alert. The probabilistic forecasts are, for instance, expressed using the cumulative

distribution function (CDF). FIG. 2 shows CDFs of hazard prediction, where the variable

X represents the hazard intensity. The CDF can be transformed into yes/no value, i.e.,

hazard alert by using two external parameters, a threshold of hazard intensity (XT) and a

threshold of excess probability (PT). These parameters are determined by the decision

trade-off, and are constant for each specific decision description.

FIG. 2 provides an example of transformation into yes/no value for the case of XT

＝50, PT＝0.1. The hazard alert is “yes” for the thick CDF, because of P＞PT at X＝XT,

while it is “no” for the thin CDF. In order to transform into yes/no value using two given

parameters, the hazard prediction should be represented not in single value but with the

distribution. For the case of New York Thruway, if the snow depth prediction X (cm) in

a form of CDF is available, it can be transformed into the hazard alert using parameters

XT＝5 cm and PT＝0.3, for example.

INTEGRATION

The hazard translator integrates access to and processing of information such as

forecasts on the causes of the hazard. For this purpose, the collaboration across disparate

scientific fields, approaches, and functional skill sets is vital, that is to say “integration”.

The meteorology and the hydrology have been operated as separate islands with

respect to the flood prediction. Wada et al. (2005) explained the situation of hydrology as

follows. “Although various studies have been made on the run-off models and the

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methods of flood prediction, there has been much less discussion on the accuracy of the

QPFs, as the input of the model. This is the reason why the very-short-range QPF

(Yamada and Kunitsugu 2002) haven’t been implemented in the flood prediction.” The

situation of the meteorology side is much the same. However, new initiatives from both

sides have emerged recently. One of them is the Hydrological Ensemble Prediction

Experiment (HEPEX), which is an international effort that brings together hydrological

and meteorological communities to develop advanced probabilistic hydrological forecast

techniques that use emerging weather and climate ensemble forecasts (Schaake, et al.

2007). In this case, the processing 1 in FIG. 1 corresponds to the European Center for

Medium-Range Weather Forecasts Ensemble Prediction System, and the processing 2 to

the flood prediction model, while the medial variable corresponds to the ensemble QPFs.

The SEW is a successful achievement of integration of multi-fields including the

weather and climate enterprise (represented by the JMA, in this case), the erosion and

sediment control research (represented by the Ministry of Land, Infrastructure,

Transportation and Tourism, in this case ) and the local government services.

c. Communication

INFORMATION FLOW

Uncertainty in communication is thought to come mainly from communication

failure due to poor infrastructure. US and Japan enjoy affluent information via various

media, however, there is another source of uncertainty relating to the information flow.

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In general, information flow is classified to the “mass-dissemination” and the

“alert-delivery”. A comparison is listed on TABLE 4. While the weather information is

usually communicated by mass-dissemination, the hazard alert should be conveyed by

alert-delivery. The mass-dissemination addresses general public, while the alert-delivery

addresses individual risk-taker. As for the information form, there are alert-form and

content-form. The alert-formed information, like warnings, takes yes/no value. Content-

formed information consists of texts, tables and figures, so it requires the processing time

and ability for recipients to use it on decision-making. The information flow is also

characterized by its updating mode and behavior to access (active or passive). While the

mass-dissemination assumes periodic update and active receipt, the alert-delivery

assumes contingent updating and passive receipt with alarm call.

COMMUNICATION IN PRACTICE

In the case of Kyushu 2003, Kumamoto Prefecture operated mutually independent

and overlapping three communication networks. The first network was for weather

information such as warnings. The second network was for hydrological data acquisition

(raingauge and river level). And the third was for the information relating to sediment

disaster (DRHRI 2004). What is available as the hazard information was buried in a large

volume. Most information was content-formed and addressed indefinite recipients. When

emergency managers receive the content-formed information to general public, they have

to start their work from determining whether or not it contains any information critical to

them. Such work confuses the recipients engaging in incident management (Zhang et al.

2008).

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The content-formed information relating to sediment disaster was automatically

sent via fax instantaneously. Automatic sending via fax does not necessarily mean

passive receipt, as in this case when the personnel of the city office were not aware of it

(DRHRI 2004). Even if the recipient were aware of it, he had to check content-formed

information in detail. Only passive receipt of the alert is critical to switch over

instantaneously the operation of city office from normal to emergency.

In the case of Tokyo 2008 the JMA issued a heavy-rain advisory at 1135 LST.

The accident occurred between 1140 LST and 1150 LST, around ten minutes after the

issue of the advisory. Personnel at the site checked warning and advisory for heavy-rain

at approximately 1130 LST, and found none of them had been issued (TMBS 2008).

Only by passive receipt with alarm call they could know the advisory without delay in

this case, too.

COMMUNICATION INITIATIVES

In the case of communication based on mass-dissemination the provider often call

the recipients as “audiences”. This word indicates that the providers look upon the

recipients as general public, and that they are uncertain where the recipients are, or how

they use the information. On the side of recipients, they obtain numbers of information

with possibilities of conflicting from multiple sources in multiple ways (Lazo, et al.

2007).

In Japan, the dissemination system of disaster-related information has developed

in a producer-driven manner. A simple belief that providing a large amount of

information must be useful in emergency management is similar to the belief that good

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forecasts must lead beneficial results. The improvement of dissemination system may not

necessarily result in the improvement of societal well-being.

A disaster-related information network named “the Sediment Disaster Information

System” has been operated by Kumamoto Prefecture, at Kyushu 2003. The sediment

disaster rainfall information (SDRI) (Takahashi, et al. 2006) was send to municipal

government via this system. Takahashi, et al. (2006) found that the emergency managers

of Minamata City had no knowledge of the SDRI. While the SDRI was operated by the

civil-engineering section of Kumamoto Prefecture, the fire and disaster section, which

was assigned to the emergency management, didn’t consider the SDRI as the emergency

information. Therefore, the SDRI was out of scope of the emergency personnel of the

city. This example clearly shows the inefficiency of provider-driven networks, even the

information is useful, especially in the case developed by the providers without

cooperation.

Since the hazard is inherent to individual risk-taker, his initiative is important to

set up the information flow suitable to the hazard alert. The provider of the hazard

information takes the role as the communication broker (Feldman, et al. 2009) for the

risk-taker, by collecting useful information of diverse disciplines and integrating them

into hazard translator.

d. Probability forecasts

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Uncertainty in forecasts can be represented in probabilistic form. Since much

have been discussed on forecast uncertainty (e.g., NRC 2006; AMS 2008), we will only

add two points, which relates to the UCM.

The generation of the hazard prediction in probabilistic form requires: 1) for the

inputs of hazard translator to include uncertainty information; 2) for the hazard translator

to be able to incorporate the uncertainty of inputs to the output.

We already mentioned the requirement 1) in section 3b. As far as weather and

climate forecasts are the inputs, the probability forecast can meet this requirement. Risk-

takers are not necessarily required to be familiar with probability forecasts. Because they

are to receive the hazard alert, where probability depiction has been translated to binary.

Although probability forecasts are not easy for human brain to deal with, they are

friendly to hazard translator.

The difficulty doesn’t lie at the risk-taker side but lies at the hazard translator. The

requirement 2) to the hazard translator will be a challenge we must tackle. Three inputs in

FIG. 1 might range over multi-discipline including meteorology, and the uncertainties of

which are expressed in a variety of ways. The translator must be able to deal

appropriately with the uncertainties not only of inputs but also of the processing.

4. Management support

a. UCM-service

Several questions are emerging in the course of deliberation so far. They are

expressed from the point of view of a risk-taker: 1) Who prepares the probability

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forecasts suitable for me? 2) Who provides me with the information flow for the hazard

alert? 3) Who translates weather and climate forecasts into the hazard alert for my

decision-making? 4) Who help me to specify the decision description?

Assuming these four “who” as the same, we call it the “UCM-service.” The

UCM-service is the service to provide risk-takers with the aids in Uncertainty-Chain

Management (UCM). The role of the UCM-service is similar to that of lawyer in the suit

in law. Ordinary persons are unaccustomed to legal issues. Lawyers can help them in law

suit by expert knowledge. In a same manner, the UCM-service provides risk-takers with

professional supports in managing of weather and climate risk.

Existing risk managements may become less effective with the change of

situations, such as climate change and the development of technology. The risk-takers are

apt to overlook the change of their risk associated with the situation changes. “Climate

change poses novel risks which are often outside the range of experience, such as impacts

related to drought, heatwaves, accelerated glacier retreat and hurricane intensity (the

Intergovernmental Panel on Climate Change Working Group II 2007).” Technology is

also changing in the domain of weather and climate forecasting, as well as of

communication. Decision descriptions should be updated according to the change of

these situations. The updating would be beyond risk-takers’ ability. It is the reason why

professional support of the UCM-service is indispensable.

b. Existing opportunities

The United Nations International Strategy for Disaster Reduction (2009) reported

that progress in implementation is still slow and adaptation policy and institutional

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frameworks are largely disconnected from those created to reduce disaster risk. The

UCM-service will help risk-takers in implementing existing opportunities.

As to the development of hazard translator (cf. section 3b), it is an extreme case to

start from beginning. The other extreme is to divert existing information extensively as

the hazard information, like the case of Kyushu 2003.

There was an actual case, where emergency managers did not utilize even existing

information. The disaster of Kyushu 2003 occurred in Minamata City, Kumamoto

Prefecture. Six years before Kyushu 2003, a similar disaster occurred in Izumi City,

Kagoshima Prefecture. Minamata and Izumi are neighboring cities, though belong to

different prefectures. After Kyushu 2003, the sediment early warning (SEW) has put into

practical use in all over-Japan. Six years after Kyushu 2003, when another similar

disaster occurred in Yamaguchi Prefecture July 2009, Hofu City in Yamaguchi Prefecture

failed to utilize the SEW in their decision of evacuation counsel. Takahashi et al. (2005)

argued that lessons of disaster hadn’t spread across the boundary of prefectures. The

personnel of most municipalities, whose jobs are the service to the residents, usually have

a number of jobs. It seems definitely insufficient to let the personnel learn from the

lessons as risk-takers. This is one of the reasons why the UCM-service is needed for

enhanced use of available information and for sharing experiences.

Without the support of UCM-service, the improvement in forecasts would not

always bring in the improvement in decision-making. For example, the improvement in

the very-short-range quantitative precipitation forecasts (Yamada and Kunitsugu 2002)

will have no impacts for the risk-taker of Tokyo 2008, unless the UCM verify the

improvement of decision-making by using it. The Weather and Climate Enterprise

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(WCE) could receive social requirement through the UCM-service, and could utilize

them as the guidance for its development. To realize the value of achievements of the

WCE, the UCM-service conducts initiatives bringing together research, technology,

management and policy making into the alliance.

c. Industry

The UCM-service is quite a different kind of enterprise from the WCE despite

their intimate relations. A comparison of the UCM-service with the WCE is shown in

TABLE 5. While the WCE provides general public with forecasts, the UCM-service

provides risk-takers with services. While the forecasts are, in general, public-goods (e.g.,

Johnson and Holt. 1997; NRC 2003) and are provided by non-profit organization such as

the government agency, the services are market-goods to be commercially provided. A

type of goods, that don’t have excludability or rivalry is called the public-goods.

“Weather information is a nonrival and, to a large extent, nonexcludable commodity

(Johnson and Holt. 1997). While forecasts are disseminated to general public, the UCM-

service addresses individual risk-taker. While the WCE is based on the meteorology, the

UCM-service is based on the management.

The public-good aspect of weather information makes it difficult, if not

impossible, for commercial companies to provide the information efficiently (NRC

2003). Forecasts tailored to particular customers are called the value-added. User-specific

forecasts such as the value-added can be restricted to a paying customer. It is seemed that

this kind of forecasts could be market-goods and might be commercially provided (e.g.,

NRC 2003; Pettifer 2008). However, unless the uncertainty-chain is managed, user-

23

specification alone is not sufficient to eliminate the gap between forecasts and well-

being.

To make it clear, we show an analogy of biotechnology referring to Pisano

(2006). The compounds possibly to have therapeutic effects are called the active

ingredient. But there is less than one-in-thousand chance that the active ingredient

becomes a new drug. Between the active ingredient and the drug there are a number of

phases to pass, such as generating data on the safety and potential effectiveness, clinical

trials and the approval by the authority. Forecasts and active ingredients belong to the

domain of the science but not to the economy as a commodity.

Scientific revolutions, for example type printing, opened big possibility for well-

being. The same is true for the weather forecasts. However, it is not the science or the

technology that realize the possibility for well-being, but the industry. The publishing

industry harnessed the possibility of the printing technology. The relation between the

pharmaceutical industry and the biotechnology is analogous to this. An industry bridging

the gap between forecasts and well-being is expected. That is the UCM-service.

5. Summary and outlook

a. Summary

The potential societal value of weather and climate forecasts is not automatically

realized, because of the gap between forecasts and well-being. Despite years of

discussion, the efforts to bridge the gap has not yet born fruit. We recognize the gap as

24

the uncertainty-chain. This study has made an attempt to show the feasibility and outline

of the Uncertainty-Chain Management (UCM).

The uncertainty-chain consists of four links of uncertainty, that is, those in

forecasts, in communication, in forecast use and in decision-making. Therefore, the

comprehensive management of the uncertainty-chain is needed to manage the weather

and climate risks, and it is referred to as the UCM.

In the discussion of this study, we took notice of the following four clues: the

probability forecasts, the information flow for hazard alerts, the translation of weather

and climate forecasts into hazard alerts, and the decision descriptions. Probability

forecasts are indispensable to the translation. The risk-takers are not required to

understand and to deal with probability forecasts, because they receive the hazard alert in

yes/no value. The information flow for hazard alerts should be alert-delivery, but not be

mass-dissemination. Weather and climate forecasts are rarely good for recognizing the

hazard and for decision-making. Instead, those critical to decision-making are the hazard-

alert of yes/no value. So, weather and climate forecasts must be translated into the

hazard-alert. The integrated system for the translation is referred to as “hazard translator”,

which would be the effort of the integration of multi-fields including the Weather and

Climate Enterprise (WCE). We proposed five dimensions to well-specify the individual

decision problem and apply the word the “decision description” to specifying these

dimensions definitely.

In order to support risk-takers with the UCM, a specialized service organization

will be needed, which we referred to as the UCM-service. We expect the UCM-service to

be commercially provided. The UCM-service is quite a different enterprise from the

25

WCE, though they are closely related. The UCM-service is an industry bridging the gap

between forecasts and well-being.

b. Outlook

The emerging UCM-service will lead to a change of the WCE.

As we argued in 3b, the translation into hazard alerts requires the forecasts of

designated specification including uncertainty information. These forecasts could be

market-goods i.e., commodity, because the provision of these forecasts can be limited to

those who wish to pay. While market-goods are subdivided into production-goods and

consumption-goods, this type of forecasts is considered the production-goods. The UCM-

service has to purchase forecasts from the WCE to translate them into the hazard alert.

Therefore, a market of forecasts to meet the demand of UCM-service is likely to emerge.

The quality of forecast as the production-goods will be exposed to rigorous verification

by the expertise in the market.

Thus the UCM-service provides opportunity to break into a new service to the

WCE. We refer to the new branch of the WCE as the “Production-goods Oriented WCE

(PO-WCE)”, which provides the UCM-service with forecasts as production-goods. Three

sectors of the WCE, i.e., the public, the private and the academic often lead to friction

and inefficiencies in the forecast system (NRC 2003). This is because they are all

providing forecasts mainly as the public-goods freely available. By contrast, the

provision of the forecasts as the production-goods is likely to be the exclusive province of

the private sector, because only the private sector can flexibly meets the specific and

changing demands of the UCM-service.

26

We have shown two business opportunities, one is the UCM-service which

provides risk-takers with the aids in their UCM, and the other is the PO-WCE which is a

new branch of the WCE to provide the UCM-service with forecasts as the production-

goods. There would be no fear for the UCM-service to compete with existing WCE

including government agency, because existing WCE provides public-goods. The PO-

WCE (the new branch of the WCE) is the necessary partner of the UCM-service to bridge

the gap between forecasts and well-being.

27

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32

List of Figures

33

TABLE 1. “Uncertainty-chain.” Four sources of uncertainty between forecasts and well-

being are shown, together with typical sources. Four sources constitute the uncertainty-

chain.

Source Typical uncertainty

Forecasts Errors

Communication Information flow

Forecast use differences between needed information and provided forecasts

Decision-making Poorly specified description

34

TABLE 2. Decision description. The “basic dimensions” of decision problem are listed for

three cases of decision-making. The “decision description” is determined to specify these

dimensions.

Basic

dimension

Kyushu 2003 New York Thruway Tokyo 2008

Risk-taker City government of

Minamata

New York Thruway

Management Office

The Tokyo

Metropolitan Bureau

of Sewerage

Risk Sediment disaster Traffic flow disorder Accident

Hazard Debris flow Snowfall and icing Flash flood

Action

alternative

Evacuation council Calling crews for snow

fighting

Suspension of work

Decision

trade-off

Between safety and

false evacuation

council

Between traffic flow

and costs

Between safety and

inefficiency

35

TABLE 3. Hazard information. For three cases of TABLE 2, the hazard, the lead time

required for the decision-making, the information used or to be used in the decision-

making, and the hazard information are listed. The hazard information is the one to

foresee the hazard, which directly cause the negative consequences, in advance.

Kyushu 2003 New York Thruway Tokyo 2008

Hazard Debris flow Snowfall and icing Flash flood

Lead time One hour 30-120 min. 5 min.

Information used

or to be used

Gauge

measurements

NWS zone forecast,

Radar display

Heavy rain warning

Hazard

information

Sediment Early

Warning

- -

36

FIG. 1. “Hazard translator.” The inputs such as weather forecasts and other information

on the causes of the hazard are translated into the “hazard prediction” in probabilistic

form via several layers of processing. A conceptual image of the case for three inputs is

shown, where ○ represents an interface. The hazard prediction is transformed into the

“hazard alert” in yes/no value.

37

FIG. 2. Transformation into alert. The hazard prediction in probabilistic form can be

transformed into yes/no value depending two external parameters. Two curves are the

Cumulative Distribution Functions (CDFs) representing the hazard predictions for two

cases. The CDFs here are integration of the probability density function from ∞ to X

(hazard intensity). The vertical line shows the threshold (XT) of X, while the horizontal

line shows the threshold (PT) of excess probability (P). The alert is “yes” for the thick

CDF because of P ≥ PT at X = XT, whereas it is “no” for the thin CDF.

38

TABLE 4. Two types of information flow i.e., the “mass-dissemination” and the “alert-

delivery” are compared.

Information flow Mass-dissemination Alert-delivery

Information Weather information Hazard alert

Address General public Risk-taker

Form Contents Alert

Update Periodical Contingent

Access Positive Passive

39

TABLE 5. The “Weather and Climate Enterprise (WCE)” and the “UCM-service.”

WCE UCM-service

Products Forecasts Services

Property of goods Public-goods Market-goods

Provider Non-profit Commercial

Client General public Risk-taker

Science Meteorology Management