verification of pilot mode block level forecast (monsoon 2013) & forecast for

VerificationOf

Pilot mode Block level Forecast(Monsoon 2013)

&Forecast

For All Districts and Blocks

(Monsoon 2014)

Ashok Kumar

Introduction1. The block level forecast was started in pilot mode and put on IMD’s website during monsoon 2013.Initially in pilot mode only one district per state. 2. 37 districts one from each state (2 each from UP and Haryana) (342 blocks) had been selected from the efficient AAS units. 3. Rainfall forecast was provided by using 0.25x0.25 resolution MME technique using the general circulation models(GCMs).Forecast for cloud amount was provided from the 0.25x0.25 resolution GCM(T-574) model after synchronizing it with MME forecast. 4. Forecast for other weather parameters up to 3-days was provided by using the 9km WRF model. Forecast for other weather parameters for day-4 and day-5 was provided by using 0.25x0.25 resolution GCM(T-574) model. 5. The forecast had been tested for its skill. Pilot mode block level forecast is coming up to the mark except that certain procedural changes are to be made.6. Now the block level weather forecast would be made operational for 6500 blocks out of 6648 blocks, for which the latitude, longitude and altitude are readily available during monsoon 2014.

Work Completed1.List of 6648 blocks in India had been retrieved from Panchayati Raj Ministry’s website after discussing with them. The web site is : panchayat.gov.in. Now this information is available in the website: lgdirectory.gov.in.2. Latitude , Longitude and altitude for 6500 blocks out of 6648 blocks in India are recorded from the standard world web sites. The standard world websites are;www.worldatlas.com/aatlas/findlatlong.htm www.Veloroutes.org/elevationwww.geoplaner.com www.indiamapia.comwww.geopostcodes.com www.distancesfrom.com www.collinsmaps.com3. For rest of the blocks(around 150 only) Survey of India is approached.4. The work of getting block level maps and latitude & longitude had already been taken up with Ministry of Agriculture and NIC.5. A prototype procedure including altitude corrections had been developed for getting forecast for all the districts and blocks in India based upon Gaussian grid output from the T-574 Model.

http://www.worldatlas.com/aatlas/findlatlong.htm

http://www.veloroutes.org/elevation

http://www.geoplaner.com/

http://www.indiamapia.com/

http://www.geopostcodes.com/

http://www.distancesfrom.com/

http://www.collinsmaps.com/

6. The procedure for getting forecast for 37 districts and 342 blocks in India in pilot mode including altitude corrections based upon regular(0.25x0.25) grid output from the T-574 Model, output from 9 Km WRF model and MME forecast for rainfall is implemented on 1st June 2013.

7. The process of verification of skill of pilot mode block level weather forecast had been completed at MCs and RMCs in IMD as well as at IMD(HQ) by using the observed data obtained from RMCs/MCs.

8. The procedure for full mode block level weather forecast system that is for all the blocks had already been developed and kept ready to implement it with in the minimum stipulated time.

MMEForecast

Sr. No. State BlockParameter

PoD FAR PCD1 D3 D5 D1 D3 D5 D1 D3 D5

1 Andaman and Nicobar Mayabandar 1 1 1 0.19 0.2 0.19 81 80 81

Rangat 1 1 1 0.31 0.31 0.31 69 69 68

2 Andhra Pradesh Ramagundam 0.5 0.5 0.5 0 0.1 0.14 Gujarat Anand 0.98 0.97 0.98 0.47 0.48 0.48

5 Jammu & Kashmir Bishna 0.9 0.43 66

Satwari 0.87 0.3 72

6 Uttarakhand Gadarpur 0.93 0.93 0.97 0.34 0.33 0.34 65 67 67

7 Haryana Ambala 0.84 0.84 0.84 0.47 0.49 0.48 57 59 54

Hissar 0.4 0.92 0.81 0.12 0.68 0.69 69 55 56

8 West Bengal Bankura 0.97 0.99 0.96 0.41 0.39 0.4 59 61 60

9 Madhya Pradesh Barghat 64 65 66

10 Sikkim Martam 1 1 1 0.3 0.3 0.3 70 71 70

Pakyong 1 1 1 0.04 0.04 0.05 96 96 95

Ranka 1 1 1 0.39 0.39 0.39 61 61 60

Rhenock 1 1 1 0.1 0.1 0.11 90 90 89

11 Kerala Vellangallur 1 1 1 0.09 0.18 0.09 91 82 91

12 Lakshadweep Amini 1 1 1 0.18 0.15 0.18 82 85 82

Minicoy 1 1 1 0.29 0.29 0.21 70 70 79

Agati 1 1 1 0.24 0.21 0.24 76 71 76

Table 2.1PoD, FAR and PC for YES/NO forecast for rainfall

Using MME forecast output

Sr. No. State BlockRainfall

Correct Usable UnusableD1 D3 D5 D1 D3 D5 D1 D3 D5

1 Andaman and Nicobar

Mayabandar6.56 9.02 8.26

10.6613.93 9.09 82.79 77.05 82.64

Rangat13.11 11.48 11.57

13.93 12.3 12.4 72.95 76.23 76.03

2 Andhra Pradesh Ramagundam50 38.89 44.44

16.67 16.67 11.11 33.33 44.44 44.44

3 Assam Jorhat 4.17 1.37 9.72 10.96 84.72 87.674 Gujarat Anand 8.2 10.66 8.2 54.92 46.72 50 36.89 42.62 41.85 Jammu & Kashmir Bishna 30.33 3.28 66.39

Satwari 27.87 6.56 65.576 Uttarakhand Gadarpur

15.25 11.0216.1 5.08 5.08 7.63 79.66 83.9 76.27

7 Haryana Ambala 19.0116.81 19.66 4.96

4.2 4.27 76.03 78.99 76.07

Hissar51.24 36.97 38.46 3.31

3.36 0.85 45.45 59.66 60.68

8 West Bengal Bankura8.2 7.38 9.84

7.389.84 4.92

84.4382.79 85.25

9 Madhya Pradesh Barghat 10.7 6.7 9.4 10.7 9.2 8.5 78.5 84.0 82.110 Sikkim Martam

8.2 9.84 4.113.11 6.56 13.93 78.69

83.6181.97

Pakyong12.3 9.84 13.93

13.11 9.0210.66

74.5981.15 75.41

Ranka3.28 4.92 2.46 4.92

4.92 8.2 91.8 90.1689.34

Rhenock4.92 6.56 13.11 13.11

7.3811.48

81.97 86.0775.41

11 Kerala Vellangallur17.65 20.59 2.94

8.82 17.65 32.35 73.5361.76

64.71

12 Lakshadweep Amini11.76 11.76 2.94

14.71 14.71 20.59 73.53 73.53 76.47

Minicoy14.71 2.94 5.88

11.76 8.82 14.71 73.53 88.24 79.41

Agathy8.82 8.82 11.76

11.76 8.8220.59

79.41 82.35 67.65

13 Nagaland Dhansiripar 6.15 0 6.15 13.85 19.1813.85

78.46 79.45 78.46

Table 2.2Correct, Usable and Unusable for YES/NO forecast for rainfall

Using MME forecast output

Sr. No. State Block

CloudRMSE Correlation

D1 D3 D5 D1 D3 D5

2 Andhra Pradesh Ramagundam3 Gujarat Anand 1.94 2.08 2.19 0.55 0.44 0.33

4 Jammu & Kashmir BishnaSatwari

5 Uttarakhand Gadarpur 3.43 2.28 2.36 0.03 0.49 0.35

6 Haryana AmbalaHissar

8 Madhya Pradesh Barghat 1.89 2.14 2.11 0.55 0.35 0.39

9 Sikkim MartamPakyongRankaRhenock

10 Kerala Vellangallur 1.08 1.38 1.12 0.51 0.27 0.43

11 Lakshadweep Amini 1.49 1.8 1.72 0.43 0.37 0.38

Minicoy 1.63 1.36 1.48 0.4 0.46 0.47

Agati 1.49 1.53 1.71 0.56 0.66 0.6

12 Assam Jorhat 1.44 1.45 0.54 0.4513 Nagaland Dhansiripar 5.35 5.3 0.18 0.03

Table 2.3RMSE and Correlation for forecast for cloud amount

Using MME and T-574 model output

Sr. No. State Block

Parameter: CloudCorrect Usable Unusable

D1 D3 D5 D1 D3 D5 D1 D3 D5

2 Andhra Pradesh Ramagundam 82.9 82.9 68.6 11.4 8.6 17.1 5.7 8.6 14.3

3 Assam Jorhat 66 73 28 19 6 84 Gujarat Anand 83 78 79 10 14 11 7 8 9

5 Jammu & Kashmir Bishna

Satwari

6 Uttarakhand Gadarpur 56 75 70 12 12 15 32 13 14


9 Madhya Pradesh Barghat 80 76 76 15 14 14 5 10 10


11 Kerala Vellangallur 100 97 97 0 0 3 0 3 0

12 Lakshadweep Amini 88 85 79 9 9 18 3 6 3

Minicoy 91 91 91 6 9 6 3 0 3Agathy 91 91 82 6 9 15 3 0 3

13 Nagaland Dhansiripar 8 8 3 4 89 88

Table 2.4Correct, Usable and Unusable for forecast for cloud amount

Using MME and T-574 model output

Conclusion(i)The rainfall forecasts are generated through Muti-Model Ensemble(MME) system by using the output from JMA, UKMO,IMD and NCEP models.MME forecast essentially gives the bias free forecast as the different NWP model forecasts are regressed against the observed data. Hence the skill for YES/NO rainfall forecast is very good (Table 2.1) even for the difficult orography regions like high terrain areas and oceanic islands.

(ii)The cloud amount forecasts are generated by using the T-574 model output after synchronizing it with the MME YES/NO rainfall forecast. The skill for cloud amount forecast is also coming as very good (Table 2.3 and Table 2.4) even for the difficult orography regions.

(iii)The forecast for the rainfall amount is not good uniformly for all the areas, as the rainfall amounts may get averaged out due to the use of regression techniques. Hence the procedure for rainfall amount will be changed and T-574 model forecast for rainfall amounts after synchronizing it with MME YES/NO rainfall forecast would be given.

T-574Model

Sr. No. State Block

Forecast Days

Day1 Day3 Day5

PD FR PC PD FR PC PD FR PC

1 Rajasthan Jaipur 0.62 0.20 73.2 0.72 0.29 70.5 0.62 0.30 67.9

2 Gujarat Anand 0.89 0.45 60.0 0.79 0.38 65.2 0.76 0.37 64.3

3 Madhya Praedesh Seoni 0.93 0.27 73.2 0.89 0.29 67.9 0.84 0.33 61.6

4 Haryana Ambala 0.65 0.25 69.6 0.68 0.22 71.4 0.69 0.35 62.5


6 Karnataka Dharwad 0.25 0.04 33.0 0.14 0.07 23.2 0.12 0.20 17.9

7 Assam Jorhat 0.59 0.36 56.3 0.46 0.30 57.1 0.61 0.27 63.0

8 West Bengal Bankura 1.0 0.36 67.9 0.97 0.39 61.6 0.94 0.38 62.5

9 Nagaland Dhansiripar 0.53 0.47 45.3 0.48 0.48 45.3 0.46 0.53 40.6

10 Jammu&Kashmir Bishanh 0.82 0.46 60.0 0.74 0.47 59.0 0.80 0.44 62.5

11 Uttarakhand Gadarpur 0.31 0.37 42.0 0.59 0.26 58.3 0.58 0.30 55.6

12 Andaman&Nicobar Mayabunder 0.65 0.13 64.3 0.70 0.14 67.0 0.74 0.11 72.3

Table 3.1 PoD, FAR and PC for YES/NO Forecast for Rainfall

using T-574 model output

Sr. No. State Block

Forecast Days

Day1 Day3 Day5

CR US UN CR US UN CR US UN


2 Gujarat Anand 29.9 11.9 58.2 41.1 11.0 47.9 40.3 6.9 52.8


4 Haryana Ambala 51.3 15.4 33.3 50.0 12.5 37.5 40.0 8.6 51.4



7 Assam Jorhat 38.1 7.9 54.0 54.7 10.0 34.4 42.2 11.3 46.5






Table 3.2Correct, Usable and Unusable forecast for rainfall

Using T-574 Model output

Sr. No. State Block

Forecast Days

Day1 Day3 Day5

RMSE CORR RMSE CORR RMSE CORR

1 Rajasthan Jaipur 3.46 0.58 3.10 0.59 2.85 0.55

2 Gujarat Anand 1.70 0.69 1.75 0.60 1.75 0.57

3 Madhya Praedesh Seoni 1.74 0.73 1.92 0.63 2.00 0.51

4 Haryana Ambala 3.18 0.58 3.31 0.55 3.31 0.58

5 Andhra Pradesh Ramagundam 1.65 0.69 1.66 0.57 1.86 0.45

6 Karnataka Dharwad 0.79 0.47 0.84 0.50 0.85 0.52

7 Assam Jorhat 1.62 0.07 1.50 0.04 1.37 0.11

8 West Bengal Bankura 3.24 0.04 3.39 -0.10 3.70 -0.15

9 Nagaland Dhansiripar 1.47 0.23 1.54 0.15 1.29 0.22

10 Jammu&Kashmir Bishanh 4.65 0.58 5.01 0.49 4.71 0.47


12 Andaman&Nicobar Mayabunder 5.33 0.29 5.36 0.17 5.38 0.07

Table 3.3RMSE and Correlation for forecast for minimum temperature


Sr. No. State Block

Forecast Days

Day1 Day3 Day5



2 Gujarat Anand 54.5 26.8 18.7 59.8 20.5 19.7 56.3 23.2 20.5


4 Haryana Ambala 28.6 17.0 54.4 27.7 21.4 50.9 30.4 23.2 46.4



7 Assam Jorhat 42.9 33.9 23.2 42.9 42.9 14.2 54.5 33.9 11.6






Table 3.4Correct, Usable and Unusable for forecast for minimum temperature


Sr. No. State Block

Forecast Days

Day1 Day3 Day5



2 Gujarat Anand 1.68 0.86 2.15 0.77 2.69 0.57


4 Haryana Ambala 4.62 0.71 5.89 0.52 5.05 0.54



7 Assam Jorhat 3.81 0.23 3.57 0.19 3.62 0.32

8 West Bengal Bankura 2.57 0.54 3.03 0.47 3.43 0.38



11 Uttarakhand Gadarpur 5.96 0.0 6.19 -0.04 5.44 0.01

12 Andaman&Nicobar Mayabunder 2.74 -0.03 2.98 0.0 3.0 -0.01

Table 3.5RMSE and Correlation for forecast for maximum temperature


Sr. No. State Block

Forecast Days

Day1 Day3 Day5



2 Gujarat Anand 47.3 32.1 20.6 36.6 29.5 33.9 25.0 28.6 46.4


4 Haryana Ambala 21.4 15.2 63.4 14.3 20.5 65.2 19.6 18.8 61.6



7 Assam Jorhat 22.3 25.0 52.7 25.9 20.5 53.6 23.2 18.8 58.0






Table 3.6Correct, Usable and Unusable for forecast for maximum temperature


Conclusion(i)The forecast for rainfall amounts using T-574 model output is coming better than the MME forecast, as the unusable forecast is not so high and skill is coming good for some of the stations(Table3.1 and 3.2). Hence the rainfall forecast would be provided by using the T-574 model output after synchronizing it with the MME YES/NO rainfall forecast.

(ii) The forecast for maximum/minimum Temperatures using the T-574 model output is giving the good skill as for the RMSE and correlation is concerned(Table 3.3 and 3.5). Correct, Usable and unusable forecast is also coming good for many stations(Table 3.4 and 3.6). Although the high terrain and oceanic islands are showing the problem. Hence day-4 and day-5 forecast for maximum/minimum temperatures and also for the other weather parameters(discussed in next section 5)would be provided by using the T-574 model output and day-1 to day-3 forecast would be provided by using the WRF model output as it is marginally better(discussed in next section) than the T-574 model output.

WRFModel

Sr. No. State Block

Forecast Days

Day1 Day2 Day3

PD FR PC PD FR PC PD FR PC


2 Gujarat Anand 1.00 0.49 51.7 0.98 0.47 53.4 1.00 0.44 58.5


4 Haryana Ambala 1.00 0.41 62.7 0.95 0.40 62.7 0.94 0.41 61.0



7 Assam Jorhat 0.97 0.37 65.3 0.94 0.35 66.9 0.93 0.36 65.2






Table 4.1PoD, FAR and PC for YES/NO Forecast for Rainfall

using WRF model output

Sr. No. State Block

Forecast Days

Day1 Day2 Day3



2 Gujarat Anand 4.9 14.8 80.3 4.8 19.0 76.2 10.2 18.8 71.0


4 Haryana Ambala 24.3 13.5 62.2 23.0 14.9 62.1 20.8 16.7 62.5



7 Assam Jorhat 13.0 13.0 74.0 19.0 13.9 67.1 19.5 7.0 71.5






Table 4.2Correct, Usable and Unusable forecast for rainfall

Using WRF Model output

Sr. No. State Block

Forecast Days

Day1 Day2 Day3



2 Gujarat Anand 1.30 0.57 1.26 0.56 1.32 0.48


4 Haryana Ambala 2.15 0.60 2.26 0.52 2.37 0.51



7 Assam Jorhat 1.43 0.13 1.47 0.08 1.39 0.16

8 West Bengal Bankura 2.44 -0.17 2.68 -0.22 2.81 -0.31




12 Andaman&Nicobar Mayabunder 4.21 0.02 4.23 0.05 4.21 0.04

Table 4.3RMSE and Correlation for forecast for minimum temperature


Sr. No. State Block

Forecast Days

Day1 Day2 Day3



2 Gujarat Anand 67.8 21.2 11.0 66.9 22.9 10.2 66.1 20.3 13.6


4 Haryana Ambala 38.1 27.1 34.8 42.4 22.0 35.6 40.7 21.2 38.1



7 Assam Jorhat 52.5 35.6 11.9 53.4 34.7 11.9 60.2 28.0 11.8






Table 4.4Correct, Usable and Unusable for forecast for minimum temperature


Sr. No. State Block

Forecast Days

Day1 Day2 Day3



2 Gujarat Anand 1.59 0.85 2.17 0.78 2.27 0.76


4 Haryana Ambala 2.94 0.71 3.87 0.64 4.05 0.63



7 Assam Jorhat 2.19 0.21 1.93 0.31 2.16 0.16

8 West Bengal Bankura 2.11 0.51 2.34 0.47 2.44 0.44



11 Uttarakhand Gadarpur 4.08 -0.02 4.74 -0.02 4.78 -0.03

12 Andaman&Nicobar Mayabunder 2.15 0.20 2.3 -0.04 2.24 -0.10

Table 4.5RMSE and Correlation for forecast for maximum temperature


Sr. No. State Block

Forecast Days

Day1 Day2 Day2



2 Gujarat Anand 46.6 33.1 20.3 43.2 23.7 33.1 34.8 30.5 34.7


4 Haryana Ambala 33.0 28.0 39.0 23.0 17.8 54.2 32.2 19.5 48.3



7 Assam Jorhat 42.4 26.6 33.0 46.6 29.7 23.7 41.5 22.9 35.6






Table 4.6Correct, Usable and Unusable for forecast for maximum temperature


Conclusion (i)Although the probability of detection is coming very good for YES/NO rainfall forecast obtained from WRF model, but the false alarm rate is also higher(Table 4.1) . It had been observed that WRF model is giving rainfall on most of the days and its percentage of unusable rainfall amounts is uniformly very high for all the areas(Table 4.2). Hence this forecast could not be used for rainfall forecast.

(ii)The skill of maximum/minimum temperatures forecast by using the WRF model output is coming very good as far as the RMSE and Correlation is concerned(Table 4.3 and 4.5). Even for the correct, usable and unusable skill of the forecast the WRF model is showing a very good skill and it is marginally better than T-574 model forecast(Table 4.4 and 4.6), although difficult orography regions are showing the problems. Hence WRF model output would be used for maximum/minimum temperatures and also for the other weather parameters(discussed in next section 5) for giving the forecast for Day-1 to Day-3.

OtherWeather

Parameters

(i)For other weather parameters(other than rainfall and maximum/minimum temperatures) the forecast for Day-1 to Day-3 was provided by using the WRF model output and forecast for Day-4 and Day-5 was provided by using the T-574 model output.

Sr. No. State Block

Minimum Relative HumidityRMSE Correlation

D1 D3 D5 D1 D3 D5


4 Jammu & Kashmir Bishna 4.67 0.78Satwari 5.06 0.77







Minicoy 2.31 2.83 2.51 0.23 0.12 0.18

Agati 2.08 2.13 2.49 -0.02 -0.11 -0.07

12 Assam Jorhat 2.74 3.11 0.46 0.3413 Nagaland Dhansiripar 3.11 3.05 0.41 0.32

Table 5.1RMSE and Correlation for forecast for minimum relative humidity

Using WRF(Day-1 to 3) and T-574(Day-4 &5) Model output

Sr. No. State Block

Parameter: Minimum Relative Humidity Correct Usable Unusable

D1 D3 D5 D1 D3 D5 D1 D3 D5


3 Assam Jorhat 42 30 30 28 28 424 Gujarat Anand 52 31 26.2 30.3 32 25 17 37 48

5 Jammu & Kashmir Bishna 14 26 60

Satwari 4 26 71







Minicoy 85 85 85 15 12 15 0 3 0Agathy 94 94 85 6 6 15 0 0 0


Table 5.2Correct, Usable and Unusable for forecast for minimum relative humidity


Sr. No. State Block

Maximum Relative HumidityRMSE Correlation

D1 D3 D5 D1 D3 D5


4 Jammu & Kashmir Bishna 3.77 0.63Satwari 3.43 0.72





10 Kerala Vellangallur 2.51 2.59 2.99 -0.15 -0.25 -0.2


Minicoy 2.21 2.26 3.03 0.18 0.34 0.13

Agati 2.3 2.34 2.88 -0.1 -0.05 -0.12

12 Assam Jorhat 3.26 3.23 0.02 0.1313 Nagaland Dhansiripar 2.91 3.13 -0.1 0.002

Table 5.3RMSE and Correlation for forecast for maximum relative humidity


Sr. No. State Block

Parameter: Maximum Relative HumidityCorrect Usable Unusable

D1 D3 D5 D1 D3 D5 D1 D3 D5


3 Assam Jorhat 27 22 20.5 27 52.5 51

4 Gujarat Anand 96 95 48 4 5 43.4 0 0 8.2

5 Jammu & Kashmir Bishna 42 35 23

Satwari 60 22 18







Minicoy 91 97 62 9 3 38 0 0 0Agathy 88 91 65 12 9 35 0 0 0


Table 5.4Correct, Usable and Unusable for forecast for maximum relative humidity


Sr. No. State BlockParameter: Wind Speed

RMSE RD1 D3 D5 D1 D3 D5

1 Andhra Pradesh Ramagundam

2 Assam Jorhat

3 Gujarat Anand 7.43 8.35 13.82 0.34 0.19 0.34

4 Jammu & Kashmir Bishna 10 0.36

Satwari 10.72 0.32



7 West Bengal Bankura

8 Madhya Pradesh Barghat13.52 17.06 9.85 -0.04 -0.04 -0.01

9 Sikkim Martam

Pakyong 6.45 6.67 6.44 -0.18 0.18 0.03

RankaRhenock

10 Kerala Vellangallur 7.05 7.86 14.94 0.33 -0.07 0.10

11 Lakshadweep Amini 11.98 15.06 0.57 0.50Minicoy 14.31 0.31Agathy 9.8 8.44 0.18 0.38

12 Nagaland Dhansiripar

Table 5.5RMSE and Correlation for forecast for wind speed


Sr. No. State Block

Parameter: Wind SpeedCorrect Usable Unusable

D1 D3 D5 D1 D3 D5 D1 D3 D5

1 Andaman and Nicobar

MayabandarRangat


3 Assam Jorhat 34.43 23.77 28.69 31.97 36.89 44.26

4 Gujarat Anand 57.38 47.54 14.75 40.98 50 43.44 1.64 2.46 41.8

5 Jammu & Kashmir Bishna 38.52 52.46 9.02

Satwari 22.13 68.03 9.846 Uttarakhand Gadarpur 61.02 50 85.89 29.66 32.2 14.41 9.32 17.8 07 Haryana Ambala

Hissar8 West Bengal Bankura


10 Sikkim MartamPakyong 83.61 80.33 76.23 16.39 19.67 23.77 0 0 0

RankaRhenock


12 Lakshadweep Amini 32 29 47 26 21 44

Minicoy 24 12 44 29 32 59Agathy 56 62 59 32 29 32 12 9 9


Table 5.6Correct, Usable and Unusable for forecast for wind speedUsing WRF(Day-1 to 3) and T-574(Day-4 &5) Model output

Sr. No. State BlockParameter: Wind Direction

RMSE RD1 D3 D5 D1 D3 D5


2 Assam Jorhat

3 Gujarat Anand 52.59 54.71 49.4 0.17 0.28 0.09

4 Jammu & Kashmir Bishna 71.94

Satwari 60.69

5 Uttarakhand Gadarpur 68.62 68.1 85.09 0.08 -0.09 0.00



8 Madhya Pradesh Barghat57.8 72.4 65.8 0.94 0.95 0.96



11 Lakshadweep Amini 25.72 29.8 26.36 0.29 0.12 -0.16

Minicoy 42.2 0.16

Agathy 22.19 23.6 25.08 0.68 0.62 0.48


Table 5.7RMSE and Correlation for forecast for wind direction


Sr. No. State Block

Parameter: Wind DirectionCorrect Usable Unusable

D1 D3 D5 D1 D3 D5 D1 D3 D5


2 Assam Jorhat

3 Gujarat Anand 69.67 69.67 76.23 10.66 7.38 1.64 19.67 22.95 22.13

4 Jammu & Kashmir Bishna 8.19 10.65 81.14

Satwari 11.47 15.57 72.95

5 Uttarakhand Gadarpur 43.22 33.05 28.81 6.78 16.10 5.08 50 50.84 66.10







Minicoy 53 44 88 9 15 6 38 41 6

Agathy 88 76 82 3 18 6 9 6 12


Table 5.8Correct, Usable and Unusable for forecast for wind direction


Conclusion (i)Verification results for maximum/minimum relative humidity have shown the good skill(Table 5.1 to 5.4), although some of the difficult orography(high terrain and oceanic islands) areas have shown the problems. (ii)Verification results for wind speed have also shown the good skill(Table 5.5 to 5.6), in this case also some of the difficult orography areas have shown problems. (iii)Verification results for wind direction have not shown the good results for many stations(Table 5.7 to 5.8). This could be due to the verification procedure being followed presently. The verification procedure needs to be changed for the typical weather parameter like wind direction. Here one suggestion could be calculating the simple success rate for wind direction by putting the values into the eight quadrants and if the observed and forecasted vales lies in the same quadrant then it is success case and total number success cases divided by the total number of cases would give the success rate.

Final Conclusion (i)The rainfall forecast would be provided by using the T-574 model output after synchronizing it with the MME YES/NO rainfall forecast for all the five days(Day-1 to Day-5). (ii)The cloud amount forecast would be provided by using the T-574 model output after synchronizing it with the MME YES/NO rainfall forecast for all the five days(Day-1 to Day-5). (iii)Weather forecast for all other weather parameters including maximum and minimum temperatures would be given by using the WRF model output for Day-1 to Day-3 and by using the T-574 model output for Day-4 to Day-5. (iv)The difficult orography regions that is high terrain areas and oceanic islands have shown the problems in the skill of the forecast. This is a typical problem for which the NWP models needs to be improved based upon the results obtained from the special projects like Himalayan Meteorology and Coupled Ocean Atmospheric model. The alternative way could be trying the -Neural Network Technique for MME forecast for rainfall. -Kalman filter for Maximum/minimum temperatures. -Model Output Statistics(MOS) guidance for different weather parameters.

Future Plansi) The block level weather forecast would be made operational for 6500 blocks out of 6648 blocks, for which the latitude, longitude and altitude are readily available. The procedure had already been developed and most likely it would be tried to implement the block level weather forecast for almost all the blocks from 1st June 2014. ii) Following procedural changes in the weather forecast would be carried out in future; -Including the block level maps for putting forecast on web. -Putting the value addition table for districts showing the different type of forecasts. -Ikonical representation of forecast for rainfall and max./min. temperature. -Putting the Meteograms for all the districts and blocks on web.iii) Kalman Filter technique would be applied for getting the bias free Maximum/Minimum temperature forecast after the regular grid (0.25 degree resolution) temperature data would become available.iv) Neural Network technique would be applied for improving the MME technique used for rainfall forecast.v) The procedure for the block level weather forecast would be developed and implemented again by using the output form the high resolution(10-12Km) GCM, when it would become operational.

vi) Verification at each grid point for the Indian region would be carried out for rainfall and maximum/minimum temperature, once the regular grid data would become available. vii) These procedures for bias free block level location specific weather forecast would be tried for other weather parameters as well. viii) After the success of block level weather forecast, location specific weather forecast would be attempted for over 2 lakh village panchayat using the WRF model output at 9 Km resolution for three days.

ix) Developing these procedures into an expert system for getting the forecast and the meteogram for any place with the specific latitude, longitude and altitude.

x) Automation of forecast bulletin and agro-advisory for block and village panchayat level on mobile.

xi) Redevelopment of the above mentioned procedures and regeneration of the forecasts required due to the up-gradation of the GCM to the higher resolution.

District and Block Level ForecastAnd

Its VerificationFor

Monsoon 2014

District And Block Level Forecast -Rainfall forecast would be provided after synchronizing the MME forecast with the T-574 model forecast.

-Cloud amount forecast would also be provided based upon T-574 model after synchronizing it with MME rainfall forecast.

-Altitude correction would be applied for maximum and minimum temperature.

-Day-1 and Day-2 forecast for other weather parameters would be provided from WRF model.(considering that one day would be lost in running the forecast procedures)

-Day-3,Day-4 and Day-5 forecast for other weather parameters would be provided from T-574 Model.

-All these forecasts would be generated at IMD’s HPC systems and comprehensive forecast tables containing the above mentioned comprehensive forecast for 655 districts and 6500 blocks would be generated every day and put on ftp-server and IMD’s web browser.

Forecast files from ftp-server For accessing the data one has to type on the web browser as follows; ftp://125.21.185.50 user: anonymous password: anonymousThen go to “pub” directory and then “DIST_BLOCK_LEVEL_FCST” directory which contains four directories dfcst, dfct, bfcst, bfct and each such directory contains the 35 states and states directories contains the forecast files with name as f_name00zyymmdd. To have formatted forecast files, which would be utilized for forecast verification and other studies, one has to see the following directories; /pub/DIST_BLOCK_LEVEL_FCST/dfcst/f_name00zyymmdd :-for districts /pub/DIST_BLOCK_LEVEL_FCST/bfcst/f_name00zyymmdd :-for blocks (There is “1-how-to-read” file in each of dfcst and bfcst directories as well) To have files containing the forecast tables, which would be utilized for generation of value added forecast and issuing the forecast to AAS units, one has to see the following directories; /pub/DIST_BLOCK_LEVEL_FCST/dfct/f_name00zyymmdd :- for districts /pub/DIST_BLOCK_LEVEL_FCST/bfct/f_name00zyymmdd :-for blocks

***Remark:- For getting forecast file on any particular day, the file with date stamp of previous day is to be looked into.***

ftp://125.21.185.50/

Forecast files from web browser

After opening the web site www.imd.gov.in , one has to open up the following ikons; NWP PRODUCTS SERVICES FORECAST DISTRICT AND BLOCK LEVELAnd then click on the drop down window or on map. For the time being drop down window is available and very soon district maps would also be included. Note:- On any particular day if the comprehensive forecast for districts and blocks under the directory DIST_BLOCK_LEVEL_FCST , is not available due to HPC maintenance and other unforeseen circumstances, then one can look for the earlier version of the district level forecast only in the following directory on ftp-server; /pub/DIST_ENS_FCST/files

Forecast Verification and report

Formatted forecast files are already being put on ftp-server which would be utilized for forecast verification at district and block level separately.

The verification report would be entitled “Report on verification of operational district and block level weather forecast” containing two sections one is on District level forecast and another is on block level forecast.

Please include at least 20 to 50 blocks for which observed data is available for having state wise scores. But care should be taken that only 10% blocks should be high terrain and oceanic islands and not all.

As 48 hr forecast is issued as Day-1 forecast and so on, hence care should be taken that starting from 48hr forecast would be verified as Day-1, and 144 hr forecast would be verified as Day-5 forecast.

These forecasts are for next 24hrs as shown against each date starting from 8.30 A.M. in the formatted files, where as in the forecast table files it for the previous 24 hours.

THANK YOU

verification of pilot mode block level forecast (monsoon 2013) & forecast for

Documents

mme forecast

rainfall forecast

block level maps

wrf model

weather parameters

altitude corrections

survey of india

latitude longitude