M. I. Ansari - National Centre for Medium Range Weather Forecasting

Quality ObservationsA Verification in case of GPS
based Radiosonde data
M. I. Ansari, Ranju Madan & S. Bhatia
6th WMO International Verification Methods Workshop-2014
19th
March 2014
1. Introduction
 Upper air atmospheric profiles of temperature and
humidity are crucial parameters for meteorology and
climate research [1Buehler, et al., 2004].
 Radiosonde provides the vertical profile of temperature,
humidity, wind direction & wind speed at a place and
these data are also used for calibrating the satellite
observations.
 These data are basis for the preparation of short period
averages and climatological normals of data. These are
required for defining the initial conditions of NWP
models, hence the backbone of the weather forecasting
system.
 Performance of radiosonde and the relative accuracy of
radiosonde data are subject to a great deal of scrutiny.
Introduction
 India
Meteorological
Department (IMD) has 39
operational
Radiosonde
radiowind stations in their
upper air network.
 In 2007, the modernization
of IMD was undertaken for
improvement
in
observational and analytical
capability to raise it to at
par with World’s leading
Meteorological centres.
Introduction
 In first phase 10 stations
were
upgraded
by
employing Modem make
GPS based radiosondes at
following 10 stations during
2009.
1. Portblair
2. Goa
3. Minicoy
4. Thiruvananthapuram
5. Hyderabad
6. Vishakhapatnam
7. Mohanbari
8. Patna
9. Srinagar
10. Chennai
2. METHODOLOGY
GPS radiosonde performance:- (2009-2011) –
Performance of 10 GPS radiosonde stations has
been examined using ECMWF global data
monitoring report. (Kumar et. al July 2011)
GPS radiosonde performance:- (Latest) Performance of 16 GPS radiosonde stations has
been examined using NCMRWF data monitoring
report.
METHODOLOGY
 The NCMRWF monthly data report is intended to give an overview of
the availability and quality of observations from the global observing
system. The information / analysis on data quality is based on
differences between observations and the values of most recent
forecast (first guess) of each of the parameters in the data set.
 Determining the absolute accuracy of data is very difficult because of
non availability a reference data source / instrument, which can
provide a known true value of the atmospheric conditions.
 This is due to the uncertainties caused by Meteorological variability,
spatial and temporal separation of measurements external and internal
interferences and random noise.
 A true precision or the standard deviation of a series of measured
value about a mean measured reference can be calculated and used as
a tool for quality measurements.
METHODOLOGY:
 For reasonableness of the data comparison of observations of upper
air data can be done with another sensor known to be operating
properly and in a better manner. Calculating a measure of the
uncertainty between the observations is referred to as comparabilty,
and here the comparability is the root-mean square (RMS) of a series of
differences between the observations.
 Standard deviation and the root-mean square are the major criteria for
assessment of data quality of upper air data.
 Standard deviation, (STD) & Root Mean Square (RMS) errors of the
following parameters have been obtained and analyzed;
1. Geopotential height (Z)
2. Temperature (T)
3. Zonal Wind Component
4. Meridional Wind dcomponent
3. EARLIER Studies:
 One of the major problem of Indian Radiosonde temperature
observation was its random large fluctuations on daily scale (Das
Gupta et al, 2005).
 Another report on “Quality of observations from Indian stations” in
2009 Das Gupta et al have evaluated the performance of upper air
observations of these stations by comparing with their Global Data
assimilation system (GDAS) first guess (6 Hr forecast from the model).
 It has been observed that GPS radiosonde data does not show such
large fluctuations, whereas for non GPS radiosondes, it perssisted
EARLIER Studies:
Kumar et al 2011
EARLIER Studies:
Kumar et al 2011
EARLIER Studies:
Kumar et al 2011
EARLIER Studies:
Kumar et al 2011
The Study
4. RESULTS - 500 hPa Temp
Station
Count
% Rejected
SD
BIAS
RMS
SRN
31
0
1.0
0.0
1.0
NDL
31
0
0.7
0.2
0.7
MNB
30
0
1.8
-0.2
1.8
PTN
31
0
0.9
-0.4
AHM
30
0
0.7
BHP
26
0
KOL
31
NGP
Station
Count
% Rejected
SD
BIAS
RMS
GWL
11
72
4.1
-0.9
4.2
1.0
LKO
7
85
1.3
-0.9
1.6
-0.6
0.9
GRK
10
60
1.4
-0.5
1.5
0.6
0.0
0.6
0
1.0
-0.4
1.1
GHT
8
87
2.0
-1.2
2.3
19
0
0.8
-0.4
0.9
RNC
26
30
2.8
0.7
2.8
BBS
31
0
0.8
-0.4
0.9
HYD
31
0
0.6
0.0
0.6
RPR
7
42
2.6
0.8
2.7
VSK
31
3
0.9
-0.6
1.1
MUM
12
58
2.0
-0.3
2.0
GOA
11
9
0.9
-0.7
1.1
CHN
29
0
0.6
-0.5
0.8
JGD
18
22
1.3
-0.8
1.5
PBL
29
0
0.8
-0.3
0.8
MPT
5
80
2.8
-4.3
5.2
MCY
27
0
0.7
-0.6
0.9
TRV
29
0
1.2
-0.7
1.4
MNG
6
50
2.9
-1.5
3.2
RESULTS -500 hPa-Temp
2.0
500hPa Temperature -Dec-2013-0000UTC
6.0
SD
1.8
1.6
SD
1.4
RMS
RMS
5.0
4.0
1.2
1.0
3.0
0.8
2.0
0.6
0.4
1.0
0.2
0.0
0.0
SRN NDL MNB PTN AHM BHP KOL NGP BBS HYD VSK GOA CHN PBL MCY TRV
GWL
LKO
GRK
GHT
RNC
RPR
MUM
JGD
MPT
MNG
RESULTS - 500 hPa -Z
Station
Count
% Rejected
SD
BIAS
RMS
SRN
31
0
12.8
9.5
15.9
NDL
31
0
10.5
2.8
10.9
MNB
30
0
12.0
-19.0
22.4
PTN
31
0
13.3
-0.2
13.3
AHM
29
0
7.8
12.5
14.7
BHP
26
0
7.8
4.0
8.7
KOL
31
0
7.7
3.5
8.5
NGP
19
0
4.7
7.6
9.0
BBS
31
0
6.0
7.0
9.2
HYD
31
0
6.5
11.4
13.1
VSK
31
3
12.6
-3.4
13.1
GOA
11
9
17.6
26.2
31.6
CHN
29
0
9.0
21.4
23.3
PBL
29
0
8.9
11.4
14.5
MCY
26
0
9.6
20.5
22.6
TRV
29
0
7.8
16.9
18.6
Station
Count
% Rejected
SD
BIAS
RMS
GWL
11
72
31.5
-58.6
66.6
LKO
7
85
19.4
-58.1
61.3
GRK
10
60
37.9
-52.4
64.7
GHT
8
75
40.8
-60.0
72.6
RNC
26
30
32.5
-7.4
33.4
RPR
7
42
33.5
-12.7
35.9
MUM
12
66
35.5
-63.0
72.3
JGD
18
27
25.9
-28.9
38.8
MPT
5
80
59.0
-98.8
115.1
MNG
6
50
45.5
-25.2
52.0
RESULTS – 500 hPa-Z
35.0
30.0
500hPa Geo-potential height -Dec-2013-0000UTC
140.0
500 hPa Geo-potential height-dec-2013- 0000 UTC
SD
RMS
120.0
SD
RMS
100.0
25.0
80.0
20.0
60.0
15.0
40.0
10.0
20.0
5.0
0.0
GWL
0.0
SRN NDL MNB PTN AHM BHP KOL NGP BBS HYD VSK GOA CHN PBL MCY TRV
LKO
GRK
GHT
RNC
RPR
MUM
JGD
MPT MNG
RESULTS - 500 hPa-Zonal Wind
Station
Count
% Rejected
SD
BIAS
RMS
SRN
31
0
2.6
-1.7
3.1
NDL
31
0
3.2
1.7
3.6
MNB
30
0
4.9
-1.6
5.2
PTN
31
0
3.5
1.4
3.7
AHM
30
0
1.4
1.0
1.8
BHP
26
0
2.4
1.0
2.6
KOL
31
0
2.7
1.0
2.9
NGP
19
0
2.2
-0.2
2.2
BBS
31
0
2.0
1.0
2.2
HYD
31
0
2.0
0.6
2.1
VSK
31
0
2.8
0.6
2.9
GOA
11
0
2.6
0.0
2.6
CHN
29
0
2.2
-0.5
2.2
PBL
29
0
2.1
-0.9
2.2
MCY
27
0
2.6
-1.3
2.9
TRV
29
0
2.6
-2.0
3.3
Station
Count
% Rejected
SD
BIAS
RMS
GWL
5
0
1.3
1.1
1.7
GRK
5
20
2.5
-7.2
7.6
GHT
6
0
8.6
-2.8
9.0
RNC
25
32
6.3
-13.0
14.4
RPR
4
0
3.4
-2.3
4.1
MUM
5
0
2.0
-3.8
4.3
JGD
11
18
4.5
1.2
4.7
MPT
4
0
2.2
0.5
2.3
MNG
1
0
0.0
-1.2
1.2
RESULTS -500 hPa-Zonal wind
6.0
500 hPa Zonal Wind Component-Dec-2013-
16.0
500hPa Zonal wind components -Dec-2013-0000UTC
14.0
5.0
SD
12.0
4.0
RMS
3.0
10.0
8.0
SD
RMS
6.0
2.0
4.0
1.0
2.0
0.0
SRN NDL MNB PTN AHM BHP KOL NGP BBS HYD VSK GOA CHN PBL MCY TRV
0.0
GWL
GRK
GHT
RNC
RPR
MUM
JGD
MPT
MNG
RESULTS - 500 hPa-Meridional Wind
Station
Count
% Rejected
SD
BIAS
RMS
SRN
31
0
4.8
-0.6
4.8
NDL
31
0
2.6
0.1
2.6
MNB
30
0
3.0
-0.1
3.0
PTN
31
0
2.7
-1.0
2.9
AHM
30
0
3.4
-0.2
3.4
BHP
26
0
3.1
0.2
3.1
KOL
31
0
2.9
-0.2
2.9
NGP
19
0
3.3
-0.1
3.3
BBS
31
0
2.9
0.3
2.9
HYD
31
0
2.6
-0.1
2.6
VSK
31
0
3.2
-0.5
3.2
GOA
11
0
3.1
0.1
3.1
CHN
29
0
2.4
0.2
2.4
PBL
29
0
2.4
-0.2
2.4
MCY
27
0
2.3
-0.7
2.5
TRV
29
0
3.1
0.0
3.1
Station
Count
% Rejected
SD
BIAS
RMS
GWL
5
0
1.6
2.0
2.5
GRK
5
20
6.6
6.0
8.9
GHT
6
0
4.0
3.3
5.2
RNC
25
32
7.3
-1.5
7.4
RPR
4
0
0.3
-0.5
0.6
MUM
5
0
1.9
-1.0
2.1
JGD
11
18
10.5
-5.0
11.6
MPT
4
0
4.2
2.5
4.8
MNG
1
0
0.0
-0.4
0.4
RESULTS – 500 hPa-Meridional wind
14.0
6.0
500 hPa Meridional Wind Component-Dec-2013- 0000 UTC
500hPa Meridional wind components -Dec-2013-0000UTC
12.0
5.0
SD
4.0
10.0
RMS
8.0
3.0
SD
6.0
2.0
4.0
1.0
2.0
RMS
0.0
0.0
SRN NDL MNB PTN AHM BHP KOL NGP BBS HYD VSK GOA CHN PBL MCY TRV
GWL
GRK
GHT
RNC
RPR
MUM
JGD
MPT
MNG
RESULTS - 100 hPa Temp
Station
Count
% Rejected
SD
BIAS
RMS
SRN
24
0
1.2
0.3
1.2
NDL
31
0
1.3
-0.4
1.4
MNB
28
0
1.5
1.3
2.0
PTN
31
0
1.1
0.0
1.1
AHM
29
0
0.9
0.1
0.9
BHP
26
0
1.2
0.1
1.3
KOL
30
0
1.4
0.7
1.5
NGP
19
0
1.1
0.6
1.3
BBS
30
0
1.5
0.6
1.6
HYD
27
0
1.2
0.6
1.3
VSK
17
0
0.9
-0.4
0.9
GOA
11
9
1.1
-1.0
1.5
CHN
22
9
1.9
0.1
1.9
PBL
28
0
1.5
0.5
1.5
MCY
25
0
1.7
-0.4
1.7
TRV
22
0
1.6
-0.1
1.6
Station
Count
% Rejected
SD
BIAS
RMS
GRK
1
100
0.0
-0.5
0.5
RNC
9
22
8.2
3.7
9.0
RESULTS - 100 hPa Temp
2.5
100hPa Temperature -Dec-2013-0000UTC
2.0
1.5
SD
RMS
1.0
0.5
0.0
SRN
NDL
MNB
PTN
AHM
BHP
KOL
NGP
BBS
HYD
VSK
GOA
CHN
PBL
MCY
TRV
RESULTS - 100 hPa -Z
Station
Count
% Rejected
SD
BIAS
RMS
SRN
24
0
21.6
66.3
69.7
NDL
31
0
30.8
8.9
32.1
MNB
28
0
14.1
61.7
63.3
PTN
31
0
17.8
-5.8
18.7
AHM
28
0
15.3
9.7
18.1
BHP
26
0
10.3
9.5
14.0
KOL
30
0
22.3
16.2
27.6
NGP
19
0
10.1
17.0
19.8
BBS
30
0
12.5
14.5
19.1
HYD
27
0
13.3
80.7
81.8
VSK
17
0
17.9
59.1
61.8
GOA
11
9
16.3
73.9
75.7
CHN
22
9
13.1
83.5
84.6
PBL
26
0
28.6
67.8
73.6
MCY
25
0
26.8
74.9
79.6
TRV
22
0
9.4
80.4
80.9
Station
Count
% Rejected
SD
BIAS
RMS
GRK
1
100
0.0
-38.0
38.0
RNC
9
66
115.9
174.4
209.5
RESULTS - 100 hPa -Z
100.0
100hPa Geo-potential height -Dec-2013-0000UTC
90.0
80.0
70.0
60.0
SD
50.0
RMS
40.0
30.0
20.0
10.0
0.0
SRN
NDL
MNB
PTN
AHM
BHP
KOL
NGP
BBS
HYD
VSK
GOA
CHN
PBL
MCY
TRV
RESULTS - 100 hPa-Zonal Wind
Station
Count
% Rejected
SD
BIAS
RMS
SRN
24
0
2.4
-0.5
2.5
NDL
31
0
3.5
1.3
3.7
MNB
28
0
3.8
-0.6
3.8
PTN
31
0
4.0
0.9
4.1
AHM
29
0
2.9
-1.2
3.1
BHP
26
0
2.7
-1.0
2.9
KOL
30
0
3.1
-1.1
3.3
NGP
19
0
3.3
-2.3
4.0
BBS
30
0
2.7
-2.1
3.4
HYD
27
0
2.8
-1.8
3.3
VSK
17
0
3.3
-1.4
3.5
GOA
11
0
2.4
-0.8
2.5
CHN
22
0
3.7
-0.2
3.7
PBL
28
0
4.3
0.0
4.3
MCY
25
0
3.8
1.5
4.1
TRV
22
0
4.2
0.3
4.2
Station
Count
% Rejected
SD
BIAS
RMS
RNC
9
33
8.7
-6.5
10.8
GRK
1
100
0.0
14.2
14.2
RESULTS - 100 hPa-Zonal Wind
5.0
100hPa Zonal wind components -Dec-2013-0000UTC
4.5
4.0
3.5
3.0
2.5
SD
2.0
RMS
1.5
1.0
0.5
0.0
SRN
NDL
MNB
PTN
AHM
BHP
KOL
NGP
BBS
HYD
VSK
GOA
CHN
PBL
MCY
TRV
RESULTS - 100 hPa-Meridional Wind
Station
Count
% Rejected
SD
BIAS
RMS
SRN
24
0
3.1
-1.1
3.3
NDL
31
0
4.2
1.6
4.5
MNB
28
0
4.2
-0.6
4.2
PTN
31
0
4.0
-0.1
4.0
AHM
29
0
4.0
-0.1
4.0
BHP
26
0
3.6
0.4
3.6
KOL
30
0
4.4
0.1
4.4
NGP
19
0
3.9
-0.1
3.9
BBS
30
0
4.6
1.1
4.6
HYD
27
0
3.5
-0.6
3.6
VSK
17
0
3.2
-0.1
3.2
GOA
11
0
3.2
0.8
3.3
CHN
22
0
2.4
-1.5
2.8
PBL
28
0
4.5
1.0
4.6
MCY
25
0
3.6
-1.0
3.7
TRV
22
0
3.8
-1.3
4.0
Station
Count
% Rejected
SD
BIAS
RMS
RNC
9
33
11.4
-6.6
13.2
GRK
1
100
0.0
-158.5
158.5
RESULTS - 100 hPa-Meridional Wind
6.0
5.0
4.0
3.0
SD
2.0
RMS
1.0
0.0
SRN
NDL
MNB
PTN
AHM
BHP
KOL
NGP
BBS
HYD
VSK
GOA
CHN
PBL
MCY
TRV
Case of New Delhi
Parameter
Bias
Range
SD
Range
RMS
Range
500 hPa-T
0.7
-.7 to 0.2
0.2
.7 to 1.8
0.7
.6 to 1.8
500 hPa-Z
10.5
-19 to 26.2
2.8
7.7 to 17.6
10.9
8.5 to 22.6
500 hPa-Zo-Wind
3.2
-2 to 1.7
1.7
1.4 to 4.9
3.6
1.8 to 5.2
500 hPa-Mer-Wind
2.6
-1 to .2
0.1
2.3 to 4.8
2.6
2.4 to 4.8
100 hPa-T
1.3
-1.0 to .7
-0.4
.9 to 1.7
1.4
1.1 to 2.2
100 hPa-Z
30.9
-5.8 to 86.7
8.9
9.4 to 30.8
32.1
14 to 84.6
100 hPa-Zo-Wind
3.5
-2.3 to 1.5
1.3
2.4 to 4.3
3.7
2.5 to 4.2
100 hPa-Mer-Wind
4.2
-1.5 to 1.6
1.6
2.4 to 4.6
4.5
3.2 to 4.6
Conclusion
 Study reveals that Random large fluctuations have
reduced significantly.
 Root Mean Square Errors (RMSE) and bias of
temperature observations from respective guess for
different levels have reduced considerably.
 Difference (O-B) between observations (O) and first
guess (B) have reduced at all levels.
 Maximum height reported has increased significantly.
 NCMRWF GDAS model has accepted all the data received from 16
GPS based stations being within the tolerance limits of the first
guess.
 The data received from non GPS radiosondes has a rejection rate of
16 % to 85 %.
Conclusion
 The standard deviation of quality data are within a range
of 9.4 to 28.6, whereas for non GPS standard deviation is
of the order of 7.3 to 115.9.
 The bias in GPS based temperature data at 100 hPa level
are within a range of 0.1 0C to -1.0 0C, whereas for non
GPS the values are of the order of 2.6 0C to 5.0 0C.
 The RMS values of GPS based 100 hPa geo-potential
heights are within a range of 14.0 to 81.8, whereas for
non GPS RMS values are of the order of 31.8 to 209.5.
 Thus, it is inferred that GPS based radiosondes are able
to provide quality data which is the backbone for a good
quality weather forecast system.
 Preference be given to the quality observations and
continuity thereof.
REFERENCES
1.Gajendra Kumar, Ranju Madan, K.C. Sai Krishnan & P. K. Jain, 2011, Technical
and operational characteristics of GPS radiosounding system in upper air network,
MAUSAM, 62, 3, pg 403-416
2. M. Das Gupta, Someshwar Das, K. Prasanthi, P.K. Pradhan and U.C. Mohanty,
2005: Validation of Upper-air observations taken during ARMEX-I and its impact on
global analysis-forecast system, Mausam, 56, 1, 139-14.
2. M. Das Gupta, National Centre for Medium range Weather Forecasting,
(NCMRWF): Report on “Quality of observations from Indian Stations”, 2009.
3. National Centre for Medium-Range Weather Forecasts (NCMRWF), December
2013: Monthly Data Monitoring Report.

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