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Site-specific analog weather-forecast system for
northwest Himalaya, India
Dan SINGH, Amreek SINGH, Ashwagosha GANJU
Research and Development Centre, Snow and Avalanche Study Establishment, Him Parisar, Sector 37 A, Chandigarh 160036,
India
E-mail: dan@rediffmail.com
ABSTRACT. In an analog weather-forecasting procedure, recorded weather in the past analogs
corresponding to the current weather situation is used to predict future weather. Consistent with the
procedure, a theoretical framework is developed to predict weather at a specific site in the Pir Panjal
range of the northwest Himalaya, India, using surface weather observations of the past ten winters
(1991/92 to 2001/02) 3 days in advance. Weather predictions were made as snow day with quantitative
snowfall category or no-snow day, for day1 through day3. As currently deployed, the procedure
routinely provides a 3 day point weather forecast as guidance information to a weather and avalanche
forecaster. Forecasts by analog model are evaluated by the various accuracy measures achieved for an
independent dataset of three winters (2002/03 to 2004/05). The results indicate that weather forecasts
by analog model are quite reliable, in that forecast accuracy corresponds closely to the relative
frequencies of observed weather events. Moreover, qualitative weather (snow day or no-snow day) and
quantitative categorical snowfall forecasts (quantitative snowfall category for snow day) are better than
reference forecasts based on persistence and climatology for day1 predictions. Site-specific snowfall
forecast guidance may play a major role in assessing avalanche danger, and accordingly formulating an
avalanche forecast for a given area in advance.
INTRODUCTION
That analogs can be used for forecasting future weather is a
very old idea. Analog forecasting (AF) consists of searching
for analogs to the present or preceding situation and then
predicting weather for the forthcoming period. The main
advantages of its use are that it yields a real solution to a
difficult problem (Namias, 1951, 1968), is adaptable to
microclimatological influences of the forecast area and
requires far less expenditure of resources (human and com-
putational) than currently available numerical weather-
prediction models (NWPs).
Many previous studies have been made of the possibility
of weather prediction using the AF procedure on a large
spatial scale with large-scale flow pattern. The results
obtained by Lorenz (1969), Gutzler and Shukla (1984) and
Ruosteenoja (1988) were disappointing for short- and
medium-range weather forecasting using the AF procedure
in both time and space with large-scale flow pattern.
However, AF procedure has shown considerable capability
in the long-range prediction of weather and its various
elements (Barnett and Preisendorfer, 1978; Kruizinga and
Murphy, 1983; Toth, 1989).
A limited-area AF model approach for short-range
weather forecasting using 500 mbar height analyses was
proposed by Van den Dool (1989). The results of short-range
limited-area point AF procedure were in marked contrast to
those of earlier studies (Lorenz, 1969; Gutzler and Shukla,
1984; Ruosteenoja, 1988), and were encouraging.
In this paper, we study the predictability of weather (snow
day with quantitative snowfall category or no-snow day) at a
specific site (Stage II (2650 m a.s.l.); Fig. 1) in the Pir Panjal
range of the northwest Himalaya (NW-Himalaya) employing
AF procedure using surface weather parameters (see Appen-
dix A) during the winter period (November–April). Surface
weather parameters are used in this study for several
practical reasons: First, they provide weather-forecast
guidance to a weather or avalanche forecaster who has a
limited source of data in a remote area of NW-Himalaya.
Second, NWP guidance with the help of a regional
mesoscale weather simulation model, MM 5 (Srinivasan
and others, 2004; A. Singh and others, 2005), is possible
with at least a 24 hour time lag (NWP guidance is available
with the data at least 24 hours earlier than the forecast time)
due to the real-time unavailability of data for initialization of
the MM 5 model. Third, it is difficult to simulate/predict
surface weather elements such as temperature and precipi-
tation over complex mountainous terrain with the help of
NWP models (Mohanty and Dimri, 2004).
An analog model is developed using surface weather
observations of a specific site, and forecasts produced by the
AF model are verified at that site. This is mainly due to large
variability in weather and its various elements such as
temperature and precipitation over NW-Himalaya. This
variability can be attributed to the high dependency of
surface weather elements upon local topography and local
atmospheric circulations over NW-Himalaya. The purpose
of developing an area-specific analog weather-forecast
model is to capture the effect of local topography on the
weather and precipitation pattern over the area from the past
database of surface weather observations of the area, and
partially to fulfill the requirement of area-specific weather-
forecast guidance for operational avalanche forecasting
during winter. Weather forecasts assume primary import-
ance during winter in NW-Himalaya because solid precipi-
tation (snow) affects large sectors of the economy
(transportation, construction, agriculture, commerce). Snow-
fall forecast is important not only from the operational
weather-forecasting point of view, but also for avalanche
forecasting (Perla, 1970; LaChapelle, 1980). Weather-
Annals of Glaciology 49 2008224
forecast guidance at a specific site during winter can help
the avalanche forecaster to assess avalanche danger and thus
formulate the avalanche forecast for a given area.
The diversity of interactions between large-scale weather
phenomena and the local topography and their outcome
(weather or precipitation pattern) remain implicitly pre-
served in the past database of surface weather observations
of any area. Therefore, surface weather observations can be
used to predict weather over an area. Thus, our first effort is
to establish a theoretical framework and evaluate the
potential of the AF procedure as a means of predicting
weather at a specific site using surface weather parameters.
At present, AF procedure is made to predict weather in
the same fashion as predicted with the help of currently
available NWP models (snow day with snowfall amount or
no-snow day). This seems reasonable until the AF procedure
or any other weather-forecasting method can be made
credible enough to predict weather as practised (generally
preferred) for operational weather forecasting (fair weather;
cloudy weather; overcast sky). However, we envision that
the AF procedure can be made to provide objective weather-
forecast guidance in operational weather-forecasting mode.
The developed analog model is tested with an inde-
pendent dataset of three winters, and weather forecasts
produced by the model are compared with the standard
reference forecasts based on persistence (hereafter persist-
ence forecast) and climatology (hereafter climatological
forecast) for day1 predictions. The climatological forecast of
the day under consideration is the mean snowfall recorded
on the same day (Julian day) in past years of the dataset. It is
a constant forecast for all three days for the independent
dataset. A persistence forecast for day1 consists of the
recorded snowfall 1 day before the day in question (e.g.
today). Persistence forecasts produced on this basis for day2
and day3 may lead to very inaccurate predictions. Therefore,
only the persistence forecast for day1 is considered.
The results of this study indicate that reasonable forecast
accuracy is possible for weather at a specific site using AF
procedure with surface weather parameters. Weather fore-
casts produced by the analog model are better than forecasts
based on persistence and climatology for day1 predictions at
a specific site.
STUDY AREA AND DATA USED
The present study area falls in the Pir Panjal range of NW-
Himalaya. Surface weather observations taken for the
development of an analog model belong to the Stage II
observatory situated on a highway connecting the Chowki-
bal valley with Tangdhar at Nasta-Chung Pass at
3120 m a.s.l. This 40km stretch of highway has 26 registered
avalanche sites, which affects movement along the highway.
Snow, meteorological and avalanche occurrence data are
regularly collected and monitored at Stage II (Fig. 1) for
operational avalanche forecasting and snow-related studies
during winter.
Surface weather parameters such as maximum tempera-
ture and its deviation in the previous 24 hours and minimum
temperature and its deviation in the previous 24 hours (see
Appendix A) are used to search analog situations corres-
ponding to the current situation from the past database of
surface weather observations of Stage II. Surface weather
observations measured manually twice daily at 0300 UTC
and 1200 UTC in the past ten winters (winter 1991/92 to
winter 2001/02, excluding the missing data of winter 1994/
95) at Stage II are used to develop the analog model. The
model is tested with the independent dataset of three winters
(winter 2002/03 to winter 2004/05).
One of the foremost requirements for an analog search
process is a complete dataset, i.e. all the parameters are
available for all the days. Unfortunately, our dataset is not
complete (one or more parameters may be missing during
winter due to sudden instrument failure). Replacement or
repair of the instrument could not be undertaken due to the
remoteness of the location and the hazardous winter
conditions. The parameters are kept intact and analog
situations are searched with the missing parameter.
AN ANALOG SEARCH PROCEDURE AND MODEL
DEVELOPMENT
The analog situations are sought here by calculating the
Euclidean distance, given below, between the current-day
surface weather observations (xik ) and the surface weather
observations of the candidate analogs (xjk ) (Barnett and
Preisendorfer, 1978; Toth, 1989; Van den Dool, 1989). The
analog situations for any day are searched from the past
dataset, where the date lies within 30 Julian days of the day
under consideration (Van den Dool, 1989).
With x
k
as the vector of kmeasurements for day i,
dij ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
m
k¼1
wkxik xjk
2
v
u
u
t,
where d
ij
is the distance between day x
i
and x
j
, and w
k
is the
weight assigned to the kth parameter. Assigning weights to
Fig. 1. Chowkibal Tangdhar axis in the Pir Panjal range of NW-
Himalaya, India.
Singh and others: Analog weather-forecast system for northwest Himalaya 225
the different parameters gives relative importance to them
for searching an analog situation and is considered an expert
task (Buser, 1983). The weights assigned to the different
parameters are given in Appendix A. These weights are
assigned based on general experience of surface weather
observations and observed weather at the forecast site in the
past. For example, surface atmospheric pressure at the
reference station is assigned maximum weight, because the
drop in atmospheric pressure at the reference station is an
indirect indication of the approach of westerly disturbance
(bad weather over the area).
In the case of a perfect match, the Euclidean distance is
zero, and smaller values of Euclidean distance indicate better
analogs (Kruizinga and Murphy, 1983; Van den Dool, 1989).
In the scan of the historical dataset, the date and snowfall
recorded in the next 24 hours of ten analogs (ten lowest
distance values) are retained. These ten analogs represent
weather observed in the past under similar conditions of
surface weather observations at a given time (e.g. today).
In addition to the ten best analogs, the preceding situation
and two successive situations corresponding to each analog
situation are also searched from the past database. The
preceding and two successive situations represent the state of
weather at the previous measurement time and at two
successive measurement times corresponding to analogs. The
date and snowfall recorded in the next 24 hours corres-
ponding to the preceding and two successive situations are
retained along with the analog situations. It may be expected
that the weather events will be similar, as reported in the past
for successive situations under the similar conditions of
surface weather parameters at the same geographic location.
Therefore, analog situations are assumed to be representative
of the state of the weather for day1 (next 24 hours), and two
successive situations are assumed to be representative of the
state of the weather for day2 (next 24–48 hours) and day3
(next 48–72 hours) for the development of an analog model.
With this assumption, the probability of snowfall for day1,
day2 and day3 is calculated from the searched analog,
preceding and succeeding situations using the following
expression (Singh and Ganju, 2004):
P¼PN
r¼1ðNrþ1Þnr
PN
r¼1ðNrþ1Þ100%,
where Nis the number of analog situations (10 in the present
case), ris the rank of an analog situation (1–10 in the present
case) and n
r
is 1.0 if the rth analog situation is the snow day,
otherwise 0.0.
For calculating the probability of snowfall using the above
expression, analog situations are ranked according to their
nearness to the current situation (first analog situation is
given maximum weight and tenth analog situation is given
least weight).
While predicting weather for three consecutive days, it is
necessary to consider the preceding situations (Singh and
Ganju, 2006). This has been achieved by taking the
weighted average of probability of snowfall for that day
and that of the previous day (while forecasting for day2, the
weighted average of probability of snowfall for day1 and
day2 is taken (Singh and Ganju, 2006)). Thus, the decision
criterion of the AF system is defined as
D¼1
3Pn1
ðÞþ
2
3Pn
ðÞ,
where P
n
is the probability of snowfall for the day under
consideration and Pn1is the probability of snowfall for the
previous day. The AF system based on the value of D(>40)
produces a qualitative categorical weather forecast (snow
day or no-snow day).
Once any day is predicted as a snow day, the AF system
based upon the mean snowfall associated with the analogs
predicts the snowfall amount for day1 using the following
expression (D. Singh and others, 2005):
X¼1
10 X
10
i¼1
Xi,
where
Xis the mean snowfall in the analogs and X
i
is the
snowfall recorded in the ith analog.
The quantitative snowfall amount for day2 and day3 is
predicted based on mean snowfall in the successive situ-
ations using the above expression. The quantitative snowfall
amount reported in the preceding and successive situations is
not considered to predict snowfall amount for any day. This is
mainly due to the consideration that snowfall amount on the
day following any day may not depend on previous days’
snowfall amount (e.g. heavy snowfall is less likely after heavy
snowfall on the previous day).
The quantitative snowfall predicted for snow day by the
AF system is changed into a quantitative snowfall category,
one among the set of six disjoint snowfall categories
(Table 1), as practised for operational weather forecasting
in NW-Himalaya (Snow and Avalanche Study Establishment
weather-forecast report). The quantitative snowfall category
is assigned to any snow day to which predicted snowfall
amount belongs.
Table 1. Quantitative snowfall-forecast categories practised for operational weather forecasting in NW-Himalaya and their occurrence
statistics at Stage II
Quantitative snowfall category Category representation Interval of snowfall values Relative frequency in past ten winters
(winter 1991/92 to winter 2001/02)
cm %
Very light A 0.1–2.4 10.49
Light B 2.5–7.5 20.13
Moderate C 7.6–35.5 50.32
Nearly heavy D 35.6–64.4 12.85
Heavy E 64.5–124.4 6.21
Very heavy F >124.4 0.0
Singh and others: Analog weather-forecast system for northwest Himalaya226
The developed AF system is tested and evaluated for the
prediction of a qualitative categorical weather forecast
(snow day or no-snow day) and quantitative categorical
snowfall forecast (quantitative snowfall category for snow
day). These two schemes are adopted intentionally to find
out whether the AF system is capable of predicting snow
day, since snow-day prediction can be considered equiva-
lent to rare-event prediction, given the predominance of no-
snow days (74% of days of test data). Thus, qualitative
categorical weather forecasts are of primary concern, and
particular emphasis is placed on various statistical accuracy
measures achieved. The performance of AF procedure for a
qualitative categorical weather forecast may directly in-
dicate the utility of AF procedure for forecasting weather.
The failure of AF procedure for a qualitative categorical
weather forecast will directly imply its failure for quantita-
tive categorical snowfall prediction.
Weather forecasts based on climatology and persistence
are also changed into qualitative categorical weather and
quantitative categorical snowfall forecasts. For this purpose,
any day predicted with snowfall amount 1.0 cm (best
accuracy of snowfall measurements in the past database) by
persistence and climatological forecasts is taken as a snow
day, and otherwise a no-snow day, and quantitative snowfall
category is assigned to any snow day to which the predicted
snowfall amount belongs. This ensures proper comparison of
forecasts by the AF system, with the forecasts based on
persistence and climatology.
RESULTS
Weather forecasts by persistence, climatology and AF model
for 544 days of test winters are verified at Stage II. Statistical
accuracy measures considered for evaluation of qualitative
categorical weather forecasts are probability of detection
(POD), miss rate (MR), false alarm rate (FAR), correct non-
occurrence (C-NON), critical success index (CSI) and
percent correct (PC) (Wilks, 1995; Mohanty and Dimri,
2004), and are given in Appendix B. The bias (BIAS) is used
as a measure of over-forecast or under-forecast tendency of
all three forecast methods.
The accuracy of each method of forecasting quantitative
categorical snowfall is defined as the percentage of snowfall
category observed in the predicted snowfall category. The
forecast accuracy for quantitative categorical snowfall fore-
cast may not represent the quality of quantitative categorical
snowfall forecast produced by each forecast method.
Therefore, quantitative categorical snowfall forecast error
is considered: the percentage of snowfall categories
recorded outside 1 category of predicted snowfall cate-
gories, for each forecast method. Accurate and precise
prediction of snowfall amount is an extremely difficult and
challenging task (Charba and Klein, 1980; Roebber and
others, 2003, 2004). Therefore, more than one category
distant observation of any predicted snowfall category is
considered as a quantitative categorical snowfall forecast
error. This, however, may give only a broad idea of the
quality of the quantitative categorical snowfall forecast
produced by each forecast method.
The qualitative weather-prediction performance of persist-
ence and climatological forecasts for day1 and the analog
model for all 3 days at a specific site is given in Table 2. The
overall accuracy (PC) of the persistence forecast and analog
model is comparable and it is poor for climatological forecast
compared to that for day1 predictions (Table 2). In our
context, the overall accuracy of the qualitative weather
prediction cannot be taken as the measure of performance of
any forecast method, due to the predominance of no-snow
days (Murphy, 1996). Therefore, forecast accuracy measures
such as POD, MR, FAR, C-NON and CSI are also considered,
in addition to overall accuracy (PC). Significant differences in
the POD, MR and CSI of an analog model and the persistent
forecast for day1 predictions suggest that the analog model
predicts snow days better than does the persistence forecast
(Table 2). However, the FAR of the persistence forecast is less
than that of the analog model for day1 predictions. This
indicates that more no-snow days are predicted as snow days
by an analog model compared to the persistence forecast for
day1 predictions. Such false predictions are produced by the
analog model when significant change in the surface weather
parameters is not observed due to prevailing, departing
westerly disturbance or when subsequently moving westerly
disturbance merges with already existing, yet departing,
westerly disturbance over the area. The persistence forecast
and the analog model predict no-snow days more accurately
than snow days (high value of C-NON compared to POD) for
day1 predictions. This can be attributed to the large number
of no-snow days (74%, test data) compared to snow days.
The POD of the climatological forecast is significantly
more than the POD of the analog model and the persistence
forecast for day1 predictions. The MR of the climatological
forecast is also significantly less than that of the analog model
and the persistence forecast for day1 predictions. However,
the FAR of the climatological forecast is significantly more
Table 2. Accuracy measures of persistence, climatological and analog models for qualitative weather prediction (snow day or no-snow day)
at a specific site for independent dataset for past three winters (winter 2002/03 to winter 2004/05)
Measure* Persistence forecast Climatological forecast Analog forecast
Day1 All three days Day1 Day2 Day3
POD 0.54 0.96 0.69 0.49 0.43
MR 0.46 0.04 0.31 0.51 0.57
FAR 0.25 0.69 0.37 0.56 0.57
C-NON 0.94 0.26 0.86 0.79 0.8
CSI 0.45 0.30 0.49 0.3 0.28
PC 83.6% 44.1% 81.8% 71.5% 70.8%
BIAS 0.72 3.13 1.09 1.09 1.01
*See Appendix B.
Singh and others: Analog weather-forecast system for northwest Himalaya 227
than that of the analog model and persistence forecast for
day1 predictions. The POD and FAR of the climatological
forecast indicates the complex pattern of snowfall events
observed in the area. The climatological forecast predicts
only 26% of no-snow days correctly, reducing the overall
performance of the climatological forecast to 44.1%. The
various accuracy measures suggest that the analog model
performs best and the climatological forecast worst of the
three for qualitative weather prediction for day1 predictions.
Various accuracy measures show that the performance of
an analog model decreases with lead time (day 1 through
day 3). Decrease in the POD, CSI and PC of the analog
model is found more from day1 to day2 predictions than
from day2 to day3 predictions. Increase in the FAR and MR
is found more from day1 to day2 predictions than from day2
to day3 predictions.
The persistence forecast under-forecasts snow day, and
the analog model over-forecasts snow day, slightly more
than the unbiased forecast (BIAS ¼1.0) for day1 predic-
tions. The under-forecast tendency of the persistence fore-
cast is a more serious issue than the slight over-forecast
tendency of the analog model from an operational weather-
forecasting point of view. The over-forecast tendency of the
climatological forecast is more than twice that of the analog
model and more than three times that of the persistence
forecast. The over-forecast tendency of the analog model
does not increase significantly with the lead time.
Quantitative categorical snowfall forecast by persistence
and climatological forecasts for day1 and the analog forecast
for all three days is given in Figure 2. Quantitative categorical
snowfall-forecast accuracy of the analog model is found to be
better than the persistence and climatological forecasts for
snowfall forecast categories B, C and D for day1 predictions.
However, the analog model does not predict snowfall events
which take place rarely in the area (A and E). The overall
accuracy (PC) and forecast error of the quantitative categor-
Fig. 2. Quantitative categorical snowfall prediction by persistence, climatological and analog models.
Fig. 3. Quantitative categorical snowfall-forecast accuracy and error of persistence, climatological and analog models.
Singh and others: Analog weather-forecast system for northwest Himalaya228
ical snowfall forecast of analog, persistence and climato-
logical forecasts is given in Figure 3. The analog model shows
significantly better overall accuracy than the persistence and
climatological forecasts (Fig. 3) for day1 predictions. The
quantitative categorical snowfall-forecast error of the analog
model is also found to be less than that of the persistence and
climatological forecasts for day1 predictions. This suggests
that the analog model performs the best of the three for
quantitative categorical snowfall forecast for day1 predic-
tions (Fig. 3). The climatological forecast is least accurate,
with the maximum forecast error for quantitative categorical
snowfall-forecast for day1 predictions. The overall perform-
ance of the analog model decreases for quantitative
categorical snowfall prediction as the lead time of the
forecast increases (Fig. 3). However, quantitative categorical
snowfall-forecast error of the analog model does not increase
significantly with lead time (day1 through day3).
The analog model performs best of the three for qualitative
weather and quantitative categorical snowfall forecast for
day1 predictions. Its performance decreases for day2 and
day3 predictions for qualitative weather forecast. However,
its overall performance for quantitative categorical snowfall
forecast does not decrease significantly, and quantitative
categorical snowfall forecast error does not increase signifi-
cantly, as the lead time of forecast increases (Fig. 3). This is
interesting from an avalanche-forecasting point of view. Site-
specific snowfall forecast guidance by an analog model may
help the avalanche forecaster to assess avalanche danger and
accordingly to formulate avalanche forecast for the area.
However, at present, quantitative snowfall forecast guidance
provided by an analog model is not tied to any avalanche
prediction model. This is because avalanche forecasting also
requires predicted values of other meteorological parameters
such as temperature and wind, along with the predicted
snowfall amount. Therefore, predictions made with the help
of an analog model may help in formulating avalanche
forecast in view of weather-forecast guidance for the area.
DISCUSSION AND CONCLUSIONS
We have described some results of a study in which an AF
procedure is employed for weather prediction for three
consecutive days at a specific site in NW-Himalaya. Both
qualitative weather and quantitative categorical snowfall
forecasts are derived from the distribution of snowfall events
associated with analog, preceding and successive situations.
Attention was primarily focused on the possibility of weather
prediction at a specific site using surface weather par-
ameters. In spite of an incomplete and small dataset, the
results obtained are encouraging, realizing the complexities
of weather prediction and snowfall amount at a specific site
in the complex mountainous terrain of NW-Himalaya.
The presented AF procedure provides evidence that a
relatively simple empirical method can serve as an inde-
pendent objective weather-forecasting guidance tool at
relatively very low cost. The main conclusions of this study
are
The limited work reported here indicates that the analog
method may be a useful weather-forecast guidance tool
for a short-range and limited area. However, our short-
term and incomplete data (ten winters) have been a
severe restriction on the testing of the method. It appears
that the method will work better with a long and
complete dataset. Therefore, more emphasis is needed
on the collection of complete data.
Comparison of the analog forecast with persistence and
climatological forecasts indicates that AF procedure
performs better than persistence and climatological
forecasts for short-range weather prediction. However,
a decrease in model performance for snow-day predic-
tion and an increase in false-alarm rate become the main
concern as the lead time of the forecast increases. This
may be due to consideration being limited to surface
weather parameters, and lack of inclusion of the data
taking into account the movement of westerly disturb-
ance. Comparative study of an analog model with the
currently available NWP models may be more useful for
assessing the potential of an analog model for weather
prediction 1–3 days in advance over a limited area. Such
a study may reveal some fruitful results for the develop-
ment of an ensemble forecast scheme.
The system is portable and is not tied to any NWP model.
It may be useful for predicting weather at a specific site
with far fewer resources than is the case with currently
available NWP models. It is also possible to generate
weather forecasts in operational weather-forecasting
mode (fair weather, overcast sky, etc.), maintaining
consistency between model output and the operational
weather forecast.
The method uses atmosphere as its own model and
significantly differs from the perfect prognostic method
(Klein and others, 1959) and model output statistics
(Glahn and Lowry, 1972).
In operational forecasting, new forecasts may be re-
quired at any time of day as new information arrives from
the forecast site. As the interval between the initial time
of the NWP model and the current time at which the
forecast is needed increases, the NWP guidance may
become outdated and new information from the forecast
site will be more important for better and accurate
prediction. Such requirements can easily be handled
with the help of the AF model. Non-availability of data
for initialization of the NWP model on a real-time basis
(Roebber and others, 2004) can hamper the operational
forecasting process based on NWP models.
The output of area-specific AF models can be integrated
to produce a large-scale weather forecast, if needed,
such as a regional mesoscale weather forecast. Such
models are naturally adapted to trend and system
dynamics, are intuitive in nature and can take care of
local-scale atmospheric processes.
The demand for accurate weather forecasting at given
locations is increasing day by day, due to various socio-
economic developments. It is possible that simple empirical
weather-forecast methods can fulfill this requirement to
some extent at a low cost. Therefore, there is a need to
explore more possibilities in this area.
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APPENDIX A
SURFACE WEATHER PARAMETERS USED TO
DEVELOP AN ANALOG WEATHER-FORECAST
MODEL
APPENDIX B
VERIFICATION MEASURES USED FOR FORECAST
EVALUATION
Probability of detection (POD) = A
AþB
False alarm rate (FAR) = C
AþC
Miss rate (MR) = B
AþB
Correct non-occurrence (CNON) = D
CþD
Critical success index (CSI) = A
AþBþC
Bias for occurrence (BIAS) = AþC
AþB
Percent correct (PC) = 100 AþD
AþBþCþD%
Parameter Weight
1. Maximum temperature and maximum
temperature deviation in last 24 hours (8C)
1.0
2. Minimum temperature and minimum
temperature deviation in last 24 hours (8C)
1.0
3. Ambient temperature and ambient
temperature deviation in last 24 hours (8C)
1.0
4. Relative humidity and relative humidity
deviation in last 24 hours (%)
1.5
5. Wind speed and wind speed deviation in
last 24 hours (km h
–1
)
1.0
6. Average wind speed and average wind
speed deviation in last 24 hours (km h
–1
)
1.5
7. Surface atmospheric pressure and surface
pressure deviation in last 24 hours (hPa)
2.5
8. Sunshine duration and sunshine duration
deviation in last 24 hours (hours)
2.0
Forecast
Yes No
Observed Yes A B
No C D
Singh and others: Analog weather-forecast system for northwest Himalaya230
