Validation of PRECIS regional climate model in Bangladesh
by
Dr. Md. Nazrul Islam
(Team leader, PRECIS Working Group in Bangladesh)
Department of Physics, BUET
and
Md. Abdul Mannan, BMD
Lochan P. Devkota, SMRC
Mrs. Mehrun Nessa, SPARRSO
September 2005
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Table of Contents
Contents
List of Tables
List of Figures
Acknowledgements
Abstract
1. Introduction
2. Methodology
3. Validation of Rainfall
3.1. Monthly Rainfall
3.2. Seasonal Rainfall
3.3. Annual Rainfall
3.4. Decadal Rainfall
3.5. Long-term Rainfall
3.6. All categories Rainfall
3.7. Rainfall bias and model performance
4. Validation of Temperature
4.1. Monthly Temperature
4.2. Seasonal Temperature
4.3. Annual Temperature
4.4. Decadal Temperature
4.5. Long-term Temperature
4.6. Temperature Bias
5. Conclusions
References
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List of Tables
Table No.
Title
Seasonal rainfall in different regions calculated by PRECIS and
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rain-gauge (obs) and averages from 1961-1990.
The CRF values for Rainfall in different months at 10 selected
2a
sites throughout Bangladesh.
The same as Table 2a except for seasonal and annual.
2b
Seasonal maximum temperature in different regions calculated
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by PRECIS and observation and averages from 1961-1990.
The same as Table 3 except for minimum temperature.
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The CMaxT value for maximum temperature in different
5a
months at 10 selected sites throughout Bangladesh.
The same as Table 5a except for seasonal and annual.
5b
The CMinT value for minimum temperature in different months
6a
at 10 selected sites throughout Bangladesh.
The same as Table 6a except for seasonal and annual.
6b
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List of Figures
Figure
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Title
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Position of 26 observational sites throughout Bangladesh.
The grid mesh with 0.5º by 0.5º resolution showing missing observation
site in some grid boxes.
July 1982 rainfall obtained from PRECIS and observation.
Comparison of July 1982 rainfall obtained from PRECIS and
observation at 10 selected sites over Bangladesh.
Monthly rainfall obtained from PRECIS and observation.
The same as Fig. 4 except for seasonal rainfall.
Rainfall of 1982 obtained from PRECIS and observation.
Time sequence of annual rainfall obtained from PRECIS and
observation
The same as Fig. 7 except for decadal rainfall.
Variation of decadal rainfall in different seasons.
Spatial distribution of rainfall over Bangladesh obtained from PRECIS
and observation.
Comparison of regional rainfall amount obtained from PRECIS and
observation.
Comparison of bla, blb and blc rainfall with observational value.
Rainfall biases for era15, blsula and blnosula.
PRECIS performance for era15, blsula and blnosula rainfall.
Comparison of monthly averaged maximum and minimum temperature.
The same as Fig. 15 except for seasonal.
Time sequence of maximum and minimum temperature.
Decadal variation of maximum and minimum temperature.
Spatial distribution of maximum temperature over Bangladesh.
Comparison of maximum temperature at 10 selected sites.
The same as Fig. 19 except for minimum temperature.
The same as Fig. 20 except for minimum temperature.
Temperature biases at different sites in different months.
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Acknowledgements
The PRECIS working group in Bangladesh express their gratitude to the Department for
International Development (DFID) of UK, United Nations Development Programme (UNDP)
and Department of Environment (DoE) of Bangladesh for financial and management supports
during this study. We are very much grateful to the Hadley Centre of UK for providing PC
based PRECIS (Providing Regional Climates for Impacts Studies) model with LBC (Lateral
Boundary Condition) data. Thanks are extended to Indian Institute of Tropical Meteorology
(IITM), Pune, India for providing PRECIS outputs in DVD-ROM and Bangladesh
Meteorological Department (BMD) for providing surface observational data.
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Abstract
The validation of PRECIS regional climate model in Bangladesh is performed with the surface
observational data of rainfall and temperature (maximum and minimum) collected by the
Bangladesh Meteorological Department (BMD) at 26 observational sites throughout the country
from 1961-1990. It is found that regional analysis provides overestimation of PRECIS values in
Bangladesh whereas data extracted at some particular locations provide better performance of
PRECIS. Overall, PRECIS can calculate about 92% of surface rainfall in case of blsula. It
overestimated rainfall for blsulb and blsulc. Performance of era15 is found much better than
other baseline categories. For all baseline categories (bla, blb and blc), the performance of
PRECIS is about 90% for rainfall. PRECIS can detect about 96% and 100.3% of maximum and
minimum temperature respectively. The merits of PRECIS can be used in predicting rainfall and
temperature in Bangladesh using the look-up table proposed in this analysis.
Finally, PRECIS is adoptable in impact studies of future climate change in Bangladesh.
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1. Introduction
There is a growing demand from many countries for regional-scale climate predictions. The
Global Climate Models (GCMs) make predictions at a relatively coarse scale of a few hundred
kilometres, but to study the impacts of climate change we need to predict changes on much
smaller scales. Regional climate models (RCMs) have a much higher resolution than global
climate models and as a result provide climate information with useful local detail including
realistic extreme events. Predictions using RCMs will thus lead to substantially improved
assessments of a country’s vulnerability to climate change and how it can adapt. The Hadley
Centre of UK has developed a regional climate model named PRECIS (Providing Regional
Climates for Impacts Studies) that can be run on a PC and can be applied easily to any area of the
globe to generate detailed climate change predictions. Details of PRECIS can be found at their
website: www.metoffice.com/research/hadleycentre/models/PRECIS.html.
PRECIS has a
horizontal resolution of 50 km with 19 levels in the atmosphere (from the surface to 30 km in the
stratosphere) and four levels in the soil. The present version of PRECIS has the option to
downscale to 25 km horizontal resolution. In addition to a comprehensive representation of the
physical processes in the atmosphere and land-surface, it also includes the sulphur cycle. The
intention is to make PRECIS freely available for use by developing country scientists involved in
vulnerability and adaptation studies conducted by their governments. It is assumed that scientists
in a group of neighbouring countries can work together so that they can configure the model over
their own region and run their own regional climate change predictions. PRECIS developers
advised to work based on groups of countries, as in many cases they have similar vulnerabilities
and face similar impacts from climate change. The Institute of Tropical Meteorology (IITM),
Pune, India runs PRECIS with 50 km horizontal resolution for present climate (1961-1990) using
different base line local boundary condition (LBC) and for future scenarios (2070-2100) using
IPCC (Intergovernmental Plan on Climate Change) Special Report on Emissions Scenarios
(SRES). PRECIS is installed in Bangladesh and a test run has been done for a short period (one
and half a year). The model output is compared with that of IITM runs. Results are the same.
Then we take the advantage to use IITM PRECIS output to validate PRECIS in Bangladesh for
1961-1990 to save computing time. Once the obtained validation result give the confidence level
of simulated outputs, future scenarios will be generated and climate change will be explained
based on the present validation information. Otherwise, downscaling run has to be considered.
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2. Methodology
Intensity of rain falls in Bangladesh depends on period and location. About 2%, 20%, 62% and
16% of the annual rainfall (2200 mm) in Bangladesh occurs during winter (DJF), pre-monsoon
(MAM), monsoon (JJAS) and post-monsoon (ON) periods respectively (Islam and Uyeda, 2005).
North-eastern and south-eastern parts of the country are heavy rainfall regions compared to
western parts of the country (Islam et al., 2005). The Bangladesh Meteorological Department
(BMD) has 26 surface observation sites throughout Bangladesh as shown in Fig. 1a. The data
collected and archived by BMD are not continuous for all years and all locations. There are
many data missing in a month or sometimes many months in a year. Also the observation
network density is not well enough, somewhere observation sites are located at about 25 km
apart whereas somewhere these are at about 145 km. When the coverage of Bangladesh is
gridded say 0.5º by 0.5º as shown in Fig. 1b, there are many grids which do not contain any
observation site. For proper analysis procedure it is important to find out the exact validation of
PRECIS in Bangladesh. Taking this in mind, analysis has been done on grid-to-grid basis and
point-to-point basis, which are explained below.
(i)
Grid-to-grid basis:
In this method, observation data collected at 26 locations are
gridded using Kriging average technique. The missing data are excluded in the average.
Then the regional value is obtained for both observation and model data at four regions
named north-west (NW: 88.8-90.4ºE; 23.5-25.2ºN), north-east (NE: 90.4-92.0ºE; 23.525.2ºN), south-east (SE: 90.4-92.0ºE; 21.8-23.5ºN) and south-west (SW: 88.8-90.4ºE;
21.8-23.5ºN). The average from all four regions is considered for Bangladesh (BD: 88.892.0ºE; 21.8-25.2ºN).
Monthly, seasonal, annual, decadal and long-term analysis is
performed using data from 1961-1990. Analysis is performed on monthly rainfall data
for blsula, blnosula, monthly maximum and minimum temperature for blsula and
blnosula.
(ii)
Point-to-point basis: In this procedure data at a particular observation point is
considered as the representative of that location.
Grid value of the model data is
compared with the observed data collected at that grid. Daily rain-gauge rainfall and
temperature (maximum and minimum) collected by BMD are processed to obtain
monthly rainfall and temperatures for 1961-1990. Then monthly values are converted to
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obtain seasonal, annual, decadal as well as long-term values. Any amount of rainfall
within 3-hour has been selected as rainy day whereas a zero amount of rainfall within 24hour has been selected as rain free day. The model data of rainfall and temperature
(maximum and minimum) are extracted at the same 26 sites and then converted to
monthly, seasonal, annual, decadal and long-term values.
Out of analyzed 30 years, 4 years (1982, 1986, 1988 and 1989) datasets were found
complete for both observation and model at all 26 sites, 26 stations’ data are analyzed
with omitting the missing years, 19 stations’ data are analyzed for all 30 years with
excluding missing data.
Comparison of rainfall and temperature determined by both observation and model are
performed month-to-month, season-to-season, year-to-year, decade-to-decade and 30year-to-30-year. Regression expression has been developed for different months, seasons
and at different sites to find the amount, which is to be added or subtracted to the model
value to predict real value from the future scenarios. Country average is obtained by
taking average from all 26 sites data. In this technique, analysis is performed on monthly
rainfall data for blsula, blnosula, blsulb, blnosulb, blsulc, blnosulc, monthly maximum
and minimum temperatures for blsula and blnosula.
3. Validation of Rainfall
Rainfalls are simulated by IITM using PRECIS for different ensembles (a, b and c) and different
lateral boundary conditions (LBCs) data, which are
i)
blsula (baseline with sulphur cycle and ensembles category a)
ii)
blnosula (baseline with nosulphur cycle and ensembles category a)
iii)
blsulb (baseline with sulphur cycle and ensembles category b)
iv)
blnosulb (baseline with nosulphur cycle and ensembles category b)
v)
blsulc (baseline with sulpher cycle and ensembles category c)
vi)
blnosulc (baseline with nosulphur cycle and ensembles category c)
vii)
era15
All above mentioned PRECIS outputs are available from 1961-1990 whereas era15 is available
from 1980-1993.
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3.1. Monthly Rainfall:
Simulated monthly rainfall of July 1982 using blsula for Bangladesh and its surroundings is
shown in Fig. 2a. Rainfall is high in the northern border and southern coastal sites whereas it is
low in the western parts of the country and very low at the Bangladesh-Myanmar border. The
amounts are very high in the north-east part, centering the shillong hill of India and over the
ocean (north of the Bay of Bengal including onshore). The observed rainfall at 26 stations is
gridded and plotted as shown in Fig. 2b. The patterns of rainfall in the northern parts are similar
to simulated rainfall (Fig. 2a) whereas patterns are different in the south-west and south-east
parts. The simulated rainfall is extracted at 26 observation sites and again gridded for the
coverage of Bangladesh in the same procedure of observed data and shown in Fig. 2c. The
pattern of rainfall in the extracted data (Fig. 2c) is similar to original simulated data (Fig 2a) but
here relatively high rainfall in the north-west part of the country is absent. The era15 rainfall
(Fig. 2d) also shows the similar pattern of blsula except relatively high rainfall in the middle of
the western border site. This pattern is much similar to observed rainfall. Hence it is clear that
when data are gridded from some locations and gridded data used for the country average, the
exact amounts are reduced as well as increased at some locations. The July 1982 observed
(blnosula) rainfall obtained at NW, NE, SE, SW and BD are 9.8 (6.2), 14.4 (7.3), 17.71 (8.5), 8.5
(8.5), and 12.6 (7.3) mm/day respectively. Substantial difference is found among the regional
rainfall obtained from observation and simulation. To obtain the exact validation at a particular
location, rainfall of July 1982 obtained by simulation and observation are compared on point-topoint basis and shown in Fig. 3. The model underestimated rainfall at many locations except at a
few locations it overestimated. At some points observed values are about more than the double
of the simulated value, which are not clarified from Figs. 2a, 2c & 2d. Rainfall in Bangladesh is
a very localized phenomena and point-to-point analysis gives opportunity to obtain the exact
amount which is to be added or subtracted to the simulated value to obtain the predicted amount
at a particular location.
To obtain the overall monthly validation, rainfall determined by observation and simulation is
averaged from 10 selected locations for 1961-1990 as shown in Fig. 4. During winter (DJF) to
pre-monsoon (MAM) the model overestimated. In the beginning of monsoon month June the
amounts determined by both observation and simulation are closer: model overestimated slightly.
During the rest of monsoon period (JAS) model underestimated. In the post-monsoon (ON)
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period both amounts are almost similar. These results are consistent to the TRMM (Tropical
Rainfall Measuring Mission) as reported by Islam and Uyeda (2005). The fact is that the
characteristics of precipitation systems in this region are different in different seasons whereas
the model uses the same cloud parameterization for the entire year.
3.2. Seasonal Rainfall:
Seasonal rainfall averaged for NW, NE, SE, SW and BD regions are obtained as tabulated in
Table 1.
Table 1.
Rainfall (mm/day) averages from 1961-90
DJF
MAM
JJAS
ON
obs
blsula
blnosula
obs
blsula
blnosula
obs
blsula
blnosula
obs
blsula
blnosula
NW
NE
SE
SW
BD
0.31
0.71
0.73
3.51
6.43
6.33
9.40
7.90
7.86
3.51
3.32
3.12
0.37
0.76
0.84
5.86
9.10
9.39
11.97
8.72
8.39
5.86
3.19
3.17
0.26
0.60
0.71
3.63
6.32
6.55
12.09
11.34
10.78
3.63
3.72
3.78
0.32
0.55
0.63
2.48
4.88
5.09
7.97
10.83
10.39
2.48
3.52
3.56
0.32
0.65
0.73
3.87
6.68
6.83
10.36
9.70
9.36
3.87
3.44
3.41
BD-Bias
-----0.33
0.41
-----2.81
2.96
------0.65
-0.99
------0.43
-0.46
It can be seen that the model overestimated rainfall in winter (DJF) and pre-monsoon (MAM)
periods whereas it underestimated in monsoon (JJAS) and post-monsoon (ON) periods. One can
find large differences at a particular region such as during monsoon period at NE; the model
underestimated about 3.2 mm/day whereas the model overestimated about 2.1 mm/day at SW.
Overall, for BD, the model underestimated rainfall during monsoon period which indicates that
averages for a large area can not represent the exact amount of rainfall at a particular region.
Figure 5 shows the comparison of seasonal rainfall obtained from observation and model
simulation for blsula and blnosula from 1961-1990. The result is the same as explained in Fig. 4
and Table 1: during winter and pre-monsoon periods, model overestimated whereas during
monsoon model underestimated. During post-monsoon both techniques measure almost the
same.
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3.3. Annual Rainfall:
Figure 6 shows the comparison of annual rainfall measured by PRECIS blsula, era15 and raingauge (obs) for the year 1982. In Fig. 6a model simulated blsula shows that less rainfall strip
passed along east-west of the country while northern and southern regions show higher amounts.
Very strong rainfall is localized in the Shillong-hill area of India. When simulated rainfall is
extracted at 26 locations and gridded as in Fig. 6b, it is clear that distribution pattern changed
slightly especially at the middle of the country. This is one of the disadvantages in data gridding
from some points which are located over irregular distances. We did this because for gridding
observational data we have to do it. So for comparison same analysis procedure may provide
reduction of random error. Fig. 6c and Fig. 6d provide the rainfall from era15 and observational
data. Hence it is clear that some high rainfall pockets of observational values are not caught by
model simulation. It may be due to the 50km horizontal resolution of the simulation. High
horizontal resolution, as in a 25km PRECIS run, may provide the better result for spatial
distribution of rainfall in this heavy rainfall region.
Time sequence of annual rainfall calculated by rain-gauge (obs) and PRECIS (blsula and
blnosula) from 1961-1990 is shown in Fig. 7a. The era15 data is available to compare from 1980
to 1990 and is shown in Fig. 7b. In some years such as 1966, 1969, 1972, 1976, 1977, 1980 and
1982, the model underestimated and in other years the model overestimated. Here it is clear that
the amount of annual rainfall is year-dependent and the simulation is not always in the same
nature i.e., the bias is not systematic. So, average for long-term data may provide the reasonable
validation. It is difficult to decide which category of simulation is better because blsula, blnosula
and era15, all are not following the same trend. In some years one category is better but in other
years another category is better. Again, long-term average can provide the suggested category,
which will be discussed later.
3.4. Decadal Rainfall:
Figure 8 shows the decadal average of rainfall determined by PRECIS and observation (obs). It
is seen from observation that rainfall in Bangladesh (averages from 10St) decreased slightly from
1971-1980 but increased again from 1981-1990. This trend can not be detected well by blnosula
and blsula. So, the increase trend of rainfall in Bangladesh can not be detected by PRECIS.
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The decadal rainfall averaged for different seasons is shown in Fig. 9.
It is seen that
observational rainfall has increased trend with decade in winter and pre-monsoon seasons. For
simulation, this increased trend is not found significant in DJF and in MAM simulation shows
decreased trend. In monsoon period observation shows that rainfall decreased from 1971-80 but
it increased again from 1981-90. In this season, simulation for blsula shows the same trend of
observation whereas blnosula shows the clear increased trend. The observed rainfall amounts in
post-monsoon are almost similar for all decades. In this season the model underestimated from
1981-90 whereas model overestimate in other 2 decades.
3.5. Long-term Rainfall:
Figure 10 shows the rainfall averaged for 1961-1990 obtained from observation (Fig. 10a), blsula
(Fig. 10b) and blnosula (Fig. 10c).
For blsula and blnosula rainfalls are extracted at 26
observation sites and then gridded in the same procedure of observational data. The patterns are
almost similar with a few exceptions in the western parts of the country.
Figure 11 shows the comparison of rainfall obtained by observation and model simulation at
NW, NE, SE, SW and BD regions averaged for 1961-90. Here model rainfalls are areal-average
from original model grid meshes and observational values are processed as in Fig. 10. It is seen
that model overestimated rainfall in all regions except in NE.
In NE region number of
observation sites is a few. Therefore, higher density of the observation network is also an
important issue to obtain better validation over a region. Rainfall bias is low in NE and high in
SW regions. Overall, bias is 0.7 mm/day for BD.
Regression equation is derived for monthly, seasonal and annual averaging rainfall from 19611990 at 10 selected locations as tabulated in Table 2a and Table 2b. The proposed regression
equation for rainfall is below
ObsRF = ModelRF + CRF
(1)
where CRF is the constant amount of rainfall at a certain location. The value of rainfall (mm/day)
at a particular location as mentioned in Table 2a and Table 2b are to be added or subtracted with
the model simulated value to obtain the real amount. One can generate the future scenarios of
climate change using PRECIS. Then the look-up table, Table2a and Table 2b, can be used in
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obtaining predicted rainfall at a particular location of the country. Such type of look-up table
may be helps us for future plan in agriculture as well as water resources sectors of the country.
Table 2a.
CRF for Rainfall_1961-1990 (blsula) in mm/day
Jan
Feb
Mar
Apr
May
Ju
Jul
Aug
Sep
Oct
Nov
Dec
Barisal
Bogra
Chittgo
Coxb
Dhak
-0.42
0.21
0.51
1.51
-5.03
-2.79
4.23
4.62
2.81
1.13
0.64
0.25
-0.46
-0.50
-1.46
-4.41
-6.95
-0.91
7.63
2.96
1.82
-1.89
-0.65
-0.19
-0.39
-0.49
-0.40
1.16
-7.12
-0.66
18.70
11.16
4.52
1.89
0.14
-0.20
-0.38
-0.30
-0.49
1.16
-9.08
0.57
18.96
13.68
4.20
1.39
0.15
-0.29
-0.54
-0.17
0.43
1.84
-1.74
-0.13
6.03
4.48
3.94
1.04
0.25
-0.04
Jess
-0.42
0.15
0.29
0.37
-5.88
-3.72
2.74
3.33
0.75
-1.38
-0.11
-0.08
Rang
-0.11
-0.11
-1.12
-4.39
-4.84
1.20
8.08
3.70
3.24
-2.19
-0.41
-0.11
Satk
-0.44
0.34
0.11
0.66
-6.13
-4.87
3.10
2.65
2.35
-0.51
-0.18
0.04
Sylhe
-0.61
-1.29
-6.65
-12.12
-13.63
4.49
10.84
6.45
5.49
-2.13
-1.04
-0.30
Srim
-0.31
0.01
0.26
2.91
-12.05
-11.87
5.29
3.78
2.34
0.31
-0.01
-0.16
10StAv
-0.41
-0.22
-0.85
-1.13
-7.25
-1.87
8.56
5.68
3.15
-0.23
-0.12
-0.11
Table 2b.
CRF for Rainfall_1961-1990 (blsula) in mm/day
Coxb
Dhak
DJF
MAM
JJAS
ON
Baris
0.01
-1.00
2.22
0.89
Bogr
-0.38
-4.27
2.87
-1.27
Chitta
-0.36
-2.12
8.43
1.02
-0.32
-2.80
9.35
0.77
-0.25
0.18
3.58
0.65
Jess
-0.12
-1.74
0.77
-0.74
Rang
-0.11
-3.45
4.05
-1.30
Satk
-0.02
-1.79
0.81
-0.35
Sylhet
-0.74
-10.80
6.82
-1.59
Ann
0.64
-0.42
2.36
2.46
1.28
-0.33
0.24
-0.24
-0.87
Srimo
-0.15
-2.96
-0.12
0.15
-0.79
10StAv
-0.24
-3.08
3.88
-0.18
0.43
3.6. All categories Rainfall:
Comparison of rainfalls obtained from blsula, blsulb, blsulc, and observation and averages from
26 locations are shown in Fig. 12. In different years different ensembles either underestimated
or overestimated rainfall. However, for 30 year average, blsula underestimated rainfall whereas
blsulb and blsulc overestimated. In regional average (Fig. 11), both blsula and blnosula were
overestimated except in one region (NE). Here it is again emphasized that gridded data from
some irregular points decreased the actual value. Averages of all ensembles can provide good
agreement with observation data.
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3.7. Rainfall bias and model performance:
Rainfall biases are defined as bias = model value – observational value. Figure 13a shows
seasonal and annual biases for era15, blsula and blnosula from 1981-90. It is seen that the
simulation overestimated during DJF and MAM and underestimated during JJAS and ON, only
era15 overestimated in ON. Annual biases from 1981-90 are calculated –0.36, -0.93 and –0.69
for era15, blnosula and blsula respectively. Therefore, it may be concluded that era15 can be
calculated rainfall much closer to the observational amounts.
Figure 13b shows the same of Fig. 13a except for blsula and blnosula averages from 1961-90.
Here, again the model underestimated during monsoon period (JJAS). Hence, the model
overestimated in ON. Annual average of rainfall for longtime (1961-1990), rainfall bias for
blnosula and blsula are –0.17 and –0.1 respectively. So, long-term average shows blsula is better
in calculating rainfall compared to blnosula. The biases are much lower than that obtained for
regional analysis as explained in Fig. 11. Therefore, the model performance is obtained from
data averages for all 26 locations throughout Bangladesh as discussed in Fig. 14. One can
calculate PRECIS performance for BD, which is 13% and 14% overestimated for blnosula and
blsula respectively.
Figure 14 shows the PRECIS performance in calculating rainfall from 26 locations over
Bangladesh.
Fig. 14a shows that PRECIS can calculate 85.65%, 89.14% and 90.09% of
observed rainfall for blnosula, blsula and era15 respectively from 1980-1990. The performance
of PRECIS increased when averages from 1961-1990 as shown in Fig. 14b. Hence, PRECIS can
calculate 91.93% and 92.68% of observed rainfall for blnosula and blsula respectively. The
performance became overrated for gridded regional analysis as shown in Fig. 14c. In SW
PRECIS overrated about 49% and in overall BD it overrated about 14%. So, point-to-point
analysis and averages for all observation sites throughout the country gives the better
performance of précis in estimating rainfall in Bangladesh. Therefore, PRECIS can be used to
generate future climate scenarios and those scenarios can be used in rainfall forecasting with
some tolerance of biases at different locations of Bangladesh as explained in Table 2 and Fig. 14.
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4. Validation of Temperature
At present we have the monthly maximum and minimum temperature PRECIS output data for
blsula and blnosula. These data are compared with observational data and discussed in this
section.
4.1. Monthly Temperature:
Figure 15 shows the monthly averaged temperature (C) obtained from 10 selected observation
sites. It is seen that maximum temperature is under-calculated by PRECIS from December to
February and then it calculate almost the same to the observational value till September (Fig.
15a). After that it under-calculates again. Minimum temperature (Fig. 15b) is under-calculated
by PRECIS from December to February, then is over-calculated until September, calculates the
same in October and under-calculates in November. From figures 15, it is seen that blsula and
blnosula temperatures are similar, which are not expected. They should differ from one another.
There may be a mistake in data copying.
4.2. Seasonal Temperature:
Seasonal temperature determined by PRECIS is compared with observational value as shown in
Fig 16. As explained in Fig. 15, maximum temperature (Fig. 16a) is under calculated by
PRECIS in winter (DJF) and post-monsoon (ON) periods. It calculated the same in pre-monsoon
(MAM) and monsoon (JJAS) periods. In case of minimum temperature (Fig. 16b), PRECIS
under calculated in winter and post-monsoon periods but it over calculated in pre-monsoon and
monsoon periods.
As previously mentioned, the values from blsula and blnosula are
unexpectedly similar, therefore, only one category is analyzed from now and the data verification
is claimed to IITM, Pune, India. Once the data correction is available, different categories may
be incorporated.
The maximum and minimum temperature in different seasons and at different regions is
tabulated in Table 3 and Table 4 respectively. It is seen that maximum temperature (Table 3)
under calculated by PRECIS in the northern parts (NW and NE) whereas over calculated in the
southern parts (SE and SW) of the country. Overall for BD, it is over calculated. The magnitude
of over calculation is higher during pre-monsoon (5.68C for BD) and monsoon (4.69C for BD)
periods.
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Table 3.
Maximum temperature (C) averages from 1961-90
DJF
MAM
JJAS
ON
obs
blnosula
obs
blnosula
obs
blnosula
obs
blnosula
NW
24.02
22.34
30.73
35.05
29.97
32.20
29.97
26.61
NE
23.31
22.55
28.52
32.09
28.33
31.70
28.33
27.12
SE
20.47
23.90
24.83
31.40
24.13
30.43
24.13
27.77
SW
20.63
23.56
26.21
34.45
24.03
30.86
24.03
27.28
BD
22.10
23.09
27.56
33.25
26.60
31.29
26.60
27.20
BD-Bias
-------0.99
-------5.68
-------4.69
-------0.60
Here is the difference between the result of point-to-point and regional average analyses.
Regional analysis shows over calculation of maximum temperature in all regions for BD where
as point-to-point analysis shows under calculation of maximum temperature during DJF and ON
and almost similar values of observation in MAM and JJAS. The fact is that when observational
data gridded, the exact values reduced to make spatial distribution.
In case of minimum temperature (Table 4), it is under calculated by PRECIS in winter and over
calculated in other periods. The over calculation is high in pre-monsoon (5.3C for BD) and
monsoon (3.54C for BD) periods.
Table 4.
Minimum temperature (C) averages from 1961-90
DJF
MAM
JJAS
ON
obs
blnosula
obs
blnosula
obs
blnosula
obs
blnosula
NW
11.82
9.42
20.23
24.10
24.34
25.54
19.57
18.13
NE
11.58
9.46
19.62
22.97
23.65
24.96
18.99
18.18
SE
11.44
13.75
17.83
24.89
19.97
26.08
17.36
20.99
SW
11.05
11.70
18.19
25.07
20.28
25.74
16.84
19.38
BD
11.47
11.09
18.96
24.26
22.05
25.58
18.18
19.18
BD-Bias
--------0.38
-------5.30
-------3.54
-------0.99
4.3. Annual Temperature:
Annual temperature determined by PRECIS is compared with that obtained from observation,
averages from 1961-1990 and from 10 selected sites as shown in Fig 17. The time sequence of
both datasets is similar in trend: temperature increased with time. The model under calculated
maximum temperature in all years except in 1979. On average under calculation of maximum
temperature is –1.36C (from –0.25 to –2.23 C). For minimum temperature, similar status of
34
under calculation or over calculation is not found. Both statuses are prevailing. On average,
PRECIS over calculate minimum temperature is 0.09C (from –0.67C 0.7C). From regional
analysis of annual temperature for BD averages for 1961-1990, it is found that maximum and
minimum temperatures over calculated by model are 3.5C and 2.58C respectively. The data
obtained at 10 selected stations show that maximum and minimum temperature under calculated
by PRECIS is 1.69C and 0.08C respectively. Hence the result of regional analysis differs from
the point-to-point analysis as previously explained. Point-to-point analysis provides better result
to the observation compared to regional analysis.
4.4. Decadal Temperature:
Figure 18 shows the decadal values of maximum and minimum temperatures. The variation of
maximum temperature in decade to decade is not much significant whereas minimum
temperature increased from 1971-1880, remain same from 1981-1990. Overall from 1961-1990,
PRECIS under calculate maximum temperature and over calculate minimum temperature.
4.5. Long-term Temperature:
Figure 19a shows the spatial distribution of monthly averaged blnosula maximum temperature
(C) obtained by PRECIS. Temperature is low in and around Shillong-hill of India and east to
Bangladesh, in the border of India-Myanmar. A high temperature zone is observed in west
Bengal of India that extends up-to west border of Bangladesh. The simulated temperature is
extracted at 26 sites and displayed in Fig. 19 b. The spatial distribution is almost similar to Fig.
19a but the high temperature zone extended more east. This is due to the high resolution of color
shade. The observed temperature obtained at same 26 selected locations is displayed in Fig. 19c.
The spatial distribution is not much similar, the strip of symmetric temperature associated almost
north-south whereas for model these are almost aligned east-west.
Figure 20 shows the comparison of maximum temperature (C) obtained from observation,
blnosula and blsula at 10 different observation sites throughout Bangladesh and averages from
1961-1990. The values for blnosula and blsul are similar that was reported in previous section.
However, for all sites, model under calculate maximum temperature that differs from regional
analysis as explained in section 4.3. The fact is that when observational data gridded for a region
then the exact value changed and due to low density of observational sites throughout
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36
37
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39
Bangladesh the regional value provides the lower grade of the actual value. Therefore, to obtain
the local situation and local effect, point-to-point analysis can provide the better validation of
temperature at that location which can be applied in many purposes, especially in the agriculture
sector of the country.
Figure 21a shows the spatial distribution of minimum temperature calculated by PRECIS. As
same as maximum temperature, Fig. 21b and Fig. 21c show the distribution of minimum
temperature obtained from 26 selected points. Model data extracted and re-gridded provides the
similar distribution. The observational data (Fig. 21c) shows some pockets of high or low
temperature, which are not clearly seen in model data.
The point-to-point validation at 10 selected sites is shown in Fig. 22.
Hence minimum
temperature under calculated at all sites except 2 sites in the SE parts of the country. This is
because model simulates high minimum temperature over ocean including onshore coastal
regions. This local effect can not be obtained from regional analysis for a large coverage.
4.6. Temperature Bias:
Figure 23 shows temperature biases at different locations and in different months averages from
10 sites and from 1961-1990. It is seen that maximum temperature (Fig. 23a) under calculated
by PRECIS in most of the months except a few months in pre-monsoon and monsoon.
Importantly, all months under calculate mamixmum temperature at Chittagong, Cox,s Bazar, and
Sylhet. These are the high rainfall regions of the country. Situations are almost opposite for
minimum temperature (Fig. 23b). Over all, for annual calculation and averages for 10 sites and
from 1961-1990, maximum temperature under calculated 1.5C whereas minimum temperature
over calculated 0.06C. If the analysis is performed using data from 26 sites where missing data
re a lots, maximum and minimum temperature over calculated 4.23C and 3.0C respectively.
Hence it is clear that increase the number of data collection site with many missing data changed
the result.
One can find slightly different result for regional average. For BD, PRECIS
determined maximum and minimum temperature (blnosula) over calculated 3.5C and 2.58C
respectively. These are closer to 26 sites average values because here the same missing data are
included. Therefore, good quality of observational data is also an important factor in model
validation.
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42
43
Figure 23 also represents the local variation of maximum and minimum temperature at 10
selected locations. Using figure 23 one can prepare a look-up table for maximum and minimum
temperature validation in different months, seasons and annual value at different sites of the
country (Table 5 and Table 6).
The proposed regression equation for maximum temperature is below
ObsMaxT = ModelMaxT + CMaxT
(2)
The value for CMaxT in different months and seasons at 10 selected sites is tabulated in Table 5a
and Table 5b respectively.
Table 5a.
CMaxT for MaxT_1961-1990 (blnosula) in C
Jan
Feb
Mar
Apr
May
Ju
Jul
Aug
Sep
Oct
Nov
Dec
Barisal
2.85
0.94
-1.30
-1.58
-0.36
1.34
-0.38
0.31
0.71
1.82
4.25
4.93
Bogra
4.66
1.35
-1.87
-1.94
-0.58
0.83
-2.02
-0.64
-0.26
2.51
6.82
7.40
Chittg
2.19
1.70
1.30
0.74
1.30
1.38
0.10
0.94
0.79
1.63
3.21
3.56
Coxb
3.38
3.66
4.05
3.47
3.14
1.20
0.98
1.50
1.90
2.66
3.81
4.14
Dhak
3.79
1.54
-1.69
-2.85
-1.13
1.24
-1.22
-0.20
0.06
1.74
5.10
6.01
Jess
3.49
0.91
-3.05
-3.61
-1.36
1.97
-0.05
0.45
1.33
2.38
5.69
6.40
Rang
3.21
0.19
-2.64
-1.26
-0.36
0.04
-1.92
-0.78
-0.85
2.16
5.07
5.62
Satk
3.53
1.09
-2.52
-3.07
-0.40
2.47
0.46
0.96
1.46
2.66
5.68
6.45
Sylhe
5.49
4.24
2.54
1.99
1.55
1.92
0.30
1.34
0.95
3.73
5.18
5.27
Srim
3.41
2.20
-0.71
-0.05
-0.51
0.17
-1.52
-0.59
-0.78
1.39
3.02
4.59
10StAv
3.60
1.78
-0.59
-0.82
0.13
1.26
-0.53
0.33
0.53
2.27
4.78
5.44
Srim
3.40
-0.43
-0.68
2.20
10StAv
3.61
-0.43
0.40
3.53
0.9
1.5
Table 5b.
CMaxT for MaxT_1961-1990 (blnosula) in C
DJF
MAM
JJAS
ON
Baris
2.91
-1.08
0.50
3.04
Bogr
4.47
-1.46
-0.52
4.66
Chitta
2.48
1.11
0.80
2.42
Coxb
3.73
3.55
1.39
3.24
Dhak
3.78
-1.89
-0.03
3.42
Jess
3.60
-2.67
0.92
4.04
Rang
3.01
-1.42
-0.88
3.61
Satk
3.69
-1.99
1.34
4.17
Ann
1.1
1.4
1.6
2.8
1.0
1.2
0.7
1.6
Sylhe
5.00
2.03
1.13
4.45
2.9
Similarly, the proposed regression equation for minimum temperature is below
ObsMinT = ModelMinT + CMinT
(3)
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The value for CMinT in different months and seasons at 10 selected sites is tabulated in Table 6a
and Table 6b respectively.
Table 6a.
CMinT for MinT_1961-1990 (blnosula) in C
Jan
Feb
Mar
Apr
May
Ju
Jul
Aug
Sep
Oct
Nov
Dec
Barisal
2.71
1.32
-1.04
-2.37
-1.72
-0.13
0.01
0.37
0.79
0.78
3.38
4.06
Bogra
4.19
1.77
-1.87
-2.48
-2.16
-0.16
-0.06
0.31
0.30
0.85
4.13
5.42
Chittg
-0.92
-1.90
-2.76
-2.73
-2.66
-1.92
-1.78
-1.65
-1.44
-1.32
-0.15
0.02
Coxb
-6.31
-6.45
-5.09
-3.69
-3.69
-3.37
-3.20
-3.16
-3.23
-3.62
-3.86
-5.47
Dhak
3.91
2.32
-1.12
-1.93
-1.90
0.16
0.44
0.72
0.98
1.08
4.02
5.34
Jess
2.92
0.74
-2.27
-2.49
-2.08
-0.13
0.11
0.34
0.62
0.64
3.77
4.57
Rang
3.71
1.25
-2.92
-3.19
-2.55
-1.12
-0.61
0.25
-0.07
0.50
4.13
5.37
Satk
3.30
1.39
-1.52
-1.84
-1.93
0.16
0.28
0.67
0.90
0.97
4.10
4.87
Sylhe
4.28
2.55
-0.77
-1.69
-1.35
-0.00
0.32
0.49
0.45
0.86
3.76
5.21
Srim
1.25
-0.08
-2.86
-2.97
-2.58
-0.91
-0.68
-0.40
-0.34
-0.51
1.67
2.75
10StAv
1.90
0.29
-2.22
-2.54
-2.26
-0.74
-0.52
-0.21
-0.10
0.02
2.50
3.21
Srim
1.31
-2.80
-0.58
0.58
10StAv
1.80
-2.34
-0.39
1.26
Table 6b.
CMinT for MinT_1961-1990 (blnosula) in C
DJF
MAM
JJAS
ON
Baris
2.70
-1.71
0.26
2.08
Bogr
3.79
-2.17
0.10
2.49
Chitta
-0.93
-2.72
-1.70
-0.74
Coxb
-6.08
-4.16
-3.24
-3.74
Dhak
3.86
-1.65
0.57
2.55
Jess
2.74
-2.28
0.24
2.20
Rang
3.44
-2.89
-0.39
2.31
Satk
3.19
-1.76
0.50
2.53
Ann
0.68
0.85
-1.60
-4.26
1.17
0.56
0.39
0.95
Sylhe
4.01
-1.27
0.32
2.31
1.18
-0.47
-0.06
Using equations (2) and (3) and look-up Table 5 and Table 6 one can find the predicted
maximum and minimum temperature from PRECIS scenarios at different locations of the
country and that values can be used in Agriculture production sector especially winter crop
production plan in Bangladesh.
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5. Conclusions
Analyzing PRECIS model outputs produced by IITM from 1961-1990 for monthly rainfall
and temperature (maximum and minimum), we can draw the following conclusions:
i)
Regional analysis with gridded observational data makes PRECIS
overestimation in calculating rainfall and temperature at different regions of
the country.
ii)
Data averages from all observational sites provide reasonable validation of
PRECIS in Bangladesh.
iii)
bla provides better performance with about 10% of underestimation for
rainfall. The model over-calculated about 17% and 16% for maximum and
minimum temperature. However, the over-calculation may be reduced when
missing data sites are excluded. Hence for 10 sites, the model undercalculated 4.48% for maximum temperature and over-calculated 0.27% for
minimum temperature.
iv)
blb and blc overestimated in calculating rainfall in Bangladesh.
Finally, PRECIS can calculate about 90% of surface rainfall, 95.52% of maximum temperature
and 100.27% of minimum temperature. Using the look-up table put up in this report for different
months, seasons and years at different locations, PRECIS can be used for the key messages for
stakeholders, policy and decision makers from PRECIS generated future climate scenarios.
References:
Islam, M. N. and H. Uyeda, 2005. Comparison of TRMM 3B42 Products with Surface Rainfall over
Bangladesh, Preprints in the proceedings of International. Geoscience and Remote Sensing Symposium
(IGARSS) 2005, Seoul, South Korea, July 25-29, 4 pages, 4TH127A_05.
Islam, M. N., T. Terao, H. Uyeda, T. Hayashi and K. Kikuchi, 2005. Spatial and temporal variations of
precipitation in and around Bangladesh,” J. Meteor. Soc. of Japan, vol. 83(1), 23-41.
46